a16z(@a16z)

AI is already running across the enterprise, and securing it is now one of CISOs' biggest concerns ...

a16z (@a16z) — Wed, 28 Oct 2026 20:15:03 GMT

AI is already running across the enterprise, and securing it is now one of CISOs' biggest concerns

Already in production at their companies:
- Employee generative AI tools (92%)
- AI coding assistants or agents (83%)
- MCPs or agent-to-app integrations (55%)
- Autonomous agents with tool access or write permissions (34%)

Their biggest concerns:
- AI attacks outpacing their team (55%)
- Employees leaking data into AI tools (47%)
- AI agents exceeding permissions or getting compromised (46%)

From a16z's @OwenColby and @steph_zhang, breaking down a survey of 100 CISOs at companies with 1,000+ employees: a16z.news/p/the-new-secu…

🔗 View Quoted Tweet

💬16🔄7❤️74👀11037📊20

⚡ Powered by xgo.ing

Ben Horowitz on treating your story as your strategy, and why founders often skip writing it even th...

a16z (@a16z) — Mon, 19 Oct 2026 23:15:31 GMT

Ben Horowitz on treating your story as your strategy, and why founders often skip writing it even though it's one of the most important parts of the job:

"The story of the company is also the strategy of the company... In your mind, you're kind of adjusting that strategy a little bit at a time every day. But then a quarter later, it's actually pretty different than what you started with."

"So it's really important that you keep upgrading your story. The articulation of your story. I would definitely recommend it be in written form."

"Something that you can share with everybody in the company, and everybody you're recruiting, everybody you're trying to raise money from, all your constituents, every customer."

"The problem that I think founders have is it doesn't feel like work. Like, 'What are you doing today?' 'I'm working on my story.' That doesn't sound like work."

"Because it doesn't feel like work, people don't do it, but it's probably one of the most important parts of the job."

@bhorowitz w/ @speedrun GP @Tocelot

Your browser does not support the video tag.

🔗 View on Twitter

💬11🔄8❤️121👀13293📊34

⚡ Powered by xgo.ing

Steven Sinofsky on why it's so hard to diffuse AI into companies: most work runs on know-how that's ...

a16z (@a16z) — Mon, 19 Oct 2026 22:00:40 GMT

Steven Sinofsky on why it's so hard to diffuse AI into companies: most work runs on know-how that's hard to turn into step-by-step instructions for an agent.

"Algorithmic thinking is really, really, really hard for the vast majority of people who have jobs."

"If you were to go to any person and ask them to create a flow chart for a particular thing that they have to go do, they would probably fail at producing that flow chart."

"Say doing a marketing plan and there's 50 marketing people working on a giant product line. One person probably understands and could document the flow chart."

"So if you put one of these agents in front of people to create these things, their ability to explain to it what to do is really, really limited."

"You're basically just developing the next abstraction layer for how people interact. And developing an abstraction layer has historically, at each level of the abstraction layer, been a highly skilled, very specific individual within an organization."

@stevesi

Your browser does not support the video tag.

🔗 View on Twitter

a16z@a16z

Jean-Denis Greze, founder of @TownAI, argues AI isn't diffusing because most people aren't tinkerers: a text box isn't enough, so the product must both identify the value prop and perform the work for them.

"There's this business that rents surfboards on an island to people and then uses Town to inspect surfboards and little beach buggies, whether they've been damaged, to decide whether afterwards the person should get their deposit back."

"What makes it work there is the tinkerer... The unlock is, is there someone over there that gets AI enough to wanna tinker a solution to their problem?"

"And the problem with that is that there aren't enough tinkerers in the world, or there aren't enough people that have the time to be tinkerers to help their business. And I think that's why AI is not diffusing."

"Instead what you're doing is you're using [AI tools] as a replacement, as like a better Google search."

"If you can make a non-tinkerer into getting the same value as a tinkerer... you could be a ginormous business today because AI can solve so many business problems. You don't need more intelligence. The intelligence is there."

@jgreze@arampell

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬17🔄4❤️53👀12629📊19

⚡ Powered by xgo.ing

Jean-Denis Greze, founder of @TownAI, argues AI isn't diffusing because most people aren't tinkerers...

a16z (@a16z) — Mon, 19 Oct 2026 20:38:43 GMT

Jean-Denis Greze, founder of @TownAI, argues AI isn't diffusing because most people aren't tinkerers: a text box isn't enough, so the product must both identify the value prop and perform the work for them.

"There's this business that rents surfboards on an island to people and then uses Town to inspect surfboards and little beach buggies, whether they've been damaged, to decide whether afterwards the person should get their deposit back."

"What makes it work there is the tinkerer... The unlock is, is there someone over there that gets AI enough to wanna tinker a solution to their problem?"

"And the problem with that is that there aren't enough tinkerers in the world, or there aren't enough people that have the time to be tinkerers to help their business. And I think that's why AI is not diffusing."

"Instead what you're doing is you're using [AI tools] as a replacement, as like a better Google search."

"If you can make a non-tinkerer into getting the same value as a tinkerer... you could be a ginormous business today because AI can solve so many business problems. You don't need more intelligence. The intelligence is there."

@jgreze@arampell

Your browser does not support the video tag.

🔗 View on Twitter

a16z@a16z

Jean-Denis Greze was CTO of Plaid. His next startup, an AI tax product for small businesses, brought in nearly $1M in revenue its first year, and he projected $2.5 to $3M the next. He shut it down anyway.

The reason: in the age of AI, it was never going to be venture scale. Incumbents could use the same technology to automate tax prep for their existing customers faster than a startup could win market share. Tax is also sold once a year, so he had about a one-month window to pivot before the door closed.

The pivot became Town, an AI assistant that works from your email and calendar. It found product-market fit fast enough that Ben Horowitz messaged a16z's Alex Rampell to say it "changed my life."

In this conversation, Alex and Jean-Denis cover:

- How to know when to kill a business that's working on paper
- Why Town competes with OpenAI and Anthropic, the companies it buys its tokens from
- Why the "forward deployed engineer" model gets worse as AI gets better
- How a $1/month AI routine can save a restaurant thousands
- What agents mean for Amazon, advertising and the future of shopping

0:57 How Ben Horowitz discovered Town
2:20 Why Town started with AI tax prep
3:55 Why tax couldn't be venture scale
6:30 Shutting down a working business
10:24 The customer ask that became Town
13:35 Why Town built for users before IT
16:18 AI's iPhone moment at work
21:28 Jevons paradox for executive assistants
23:13 Town as a "token allocation machine"
28:00 How Town tailors itself to each job
34:03 Competing with your token suppliers
42:24 Staying off the AI labs' radar
44:42 Why AI isn't spreading evenly yet
52:32 How agents break the ad model
55:35 The friction humans actually enjoy
57:05 Why supply aggregators lose to agents

YouTube: youtu.be/o_5f4N6Nq80@jgreze@TownAI@arampell

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬16🔄5❤️68👀21402📊19

⚡ Powered by xgo.ing

Every AI app gets asked whether the labs will crush it The mobile era offers one answer: chips led ...

a16z (@a16z) — Mon, 19 Oct 2026 19:30:03 GMT

Every AI app gets asked whether the labs will crush it

The mobile era offers one answer: chips led first, then devices, and over time software and services built on top pulled ahead

a16z@a16z

Jean-Denis Greze, founder of @TownAI, on the question every AI app founder gets asked: what stops the big labs from crushing you?

"It is risky always to be competing with your suppliers, right? We're competing against our suppliers. So you gotta ask yourself, is that naturally the right state of the world?"

"Someone at OpenAI, at Anthropic, at Google could wake up and decide to do that. So the better answer actually is to think about our customers and what they want."

"It is our job to determine from all the AI capabilities, frontier model, open weight, what is the right thing for you as a business. And I think you can save money there, but also you can de-risk ever having made the wrong partner."

"There are companies that picked OpenAI like two years ago, and then Anthropic was better at coding for a 12-month period. And those companies had to use both because they realized to be competitive, they needed Claude."

"If you're a smart business, you really think about the neutrality of who's providing intelligence for you and who's routing."

@jgreze@arampell

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬13🔄13❤️150👀21068📊28

⚡ Powered by xgo.ing

Jean-Denis Greze, founder of @TownAI, on the question that made him kill his first startup despite n...

a16z (@a16z) — Mon, 19 Oct 2026 18:20:11 GMT

Jean-Denis Greze, founder of @TownAI, on the question that made him kill his first startup despite nearly $1M in first-year revenue: can you outrun the incumbents using the same technology?

"After one year, we had what I would describe as a good business pre-AI... But post-AI, this was not gonna be a venture scale company."

"We realized that the incumbents were gonna be able to use the technology to automate tax prep for their existing customers faster than we could grab market share. That's like a key thing if you're a founder you have to think about."

"Your advantage if you're a new entrant is you're faster moving, you can build a different product, you can use different go-to-market approaches, but there's a ticking clock on the wall."

"It felt for tax like we wouldn't be able to get to hundreds of millions of revenue before our competitors, the incumbents could, because you can only sell tax once a year... It's rare for people to brag about their tax prep situation."

@jgreze@arampell

Your browser does not support the video tag.

🔗 View on Twitter

a16z@a16z

Jean-Denis Greze was CTO of Plaid. His next startup, an AI tax product for small businesses, brought in nearly $1M in revenue its first year, and he projected $2.5 to $3M the next. He shut it down anyway.

The reason: in the age of AI, it was never going to be venture scale. Incumbents could use the same technology to automate tax prep for their existing customers faster than a startup could win market share. Tax is also sold once a year, so he had about a one-month window to pivot before the door closed.

The pivot became Town, an AI assistant that works from your email and calendar. It found product-market fit fast enough that Ben Horowitz messaged a16z's Alex Rampell to say it "changed my life."

In this conversation, Alex and Jean-Denis cover:

- How to know when to kill a business that's working on paper
- Why Town competes with OpenAI and Anthropic, the companies it buys its tokens from
- Why the "forward deployed engineer" model gets worse as AI gets better
- How a $1/month AI routine can save a restaurant thousands
- What agents mean for Amazon, advertising and the future of shopping

0:57 How Ben Horowitz discovered Town
2:20 Why Town started with AI tax prep
3:55 Why tax couldn't be venture scale
6:30 Shutting down a working business
10:24 The customer ask that became Town
13:35 Why Town built for users before IT
16:18 AI's iPhone moment at work
21:28 Jevons paradox for executive assistants
23:13 Town as a "token allocation machine"
28:00 How Town tailors itself to each job
34:03 Competing with your token suppliers
42:24 Staying off the AI labs' radar
44:42 Why AI isn't spreading evenly yet
52:32 How agents break the ad model
55:35 The friction humans actually enjoy
57:05 Why supply aggregators lose to agents

YouTube: youtu.be/o_5f4N6Nq80@jgreze@TownAI@arampell

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬10🔄6❤️66👀14658📊14

⚡ Powered by xgo.ing

Eugenia Kuyda, founder of @Wabi, pitching "the one friend you wish you had in every conversation" 10...

a16z (@a16z) — Mon, 19 Oct 2026 16:54:35 GMT

Eugenia Kuyda, founder of @Wabi, pitching "the one friend you wish you had in every conversation" 10 years ago, long before AI agents went mainstream:

Eugenia Kuyda@ekuyda

Wabi 2.0 is live!

personal agents are one person, one chat. but the parts of life that matter run on more than one person

Wabi is an agent you use together. you talk to it, it does stuff, builds the apps you need, and anyone can join the thread - your partner, your best friend, the preschool moms groupchat

school changes pickup time → Wabi updates the family calendar, everyone sees it.
a show you’d both love → Wabi finds a night that works, gets the tickets.
the trip everyone keeps talking about → Wabi picks the dates, builds the itinerary, books it

go start a thread with your people 👇

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬9🔄3❤️50👀13976📊10

⚡ Powered by xgo.ing

Jean-Denis Greze, founder of @TownAI, on the question every AI app founder gets asked: what stops th...

a16z (@a16z) — Mon, 19 Oct 2026 16:07:25 GMT

Jean-Denis Greze, founder of @TownAI, on the question every AI app founder gets asked: what stops the big labs from crushing you?

"It is risky always to be competing with your suppliers, right? We're competing against our suppliers. So you gotta ask yourself, is that naturally the right state of the world?"

"Someone at OpenAI, at Anthropic, at Google could wake up and decide to do that. So the better answer actually is to think about our customers and what they want."

"It is our job to determine from all the AI capabilities, frontier model, open weight, what is the right thing for you as a business. And I think you can save money there, but also you can de-risk ever having made the wrong partner."

"There are companies that picked OpenAI like two years ago, and then Anthropic was better at coding for a 12-month period. And those companies had to use both because they realized to be competitive, they needed Claude."

"If you're a smart business, you really think about the neutrality of who's providing intelligence for you and who's routing."

@jgreze@arampell

Your browser does not support the video tag.

🔗 View on Twitter

a16z@a16z

Jean-Denis Greze was CTO of Plaid. His next startup, an AI tax product for small businesses, brought in nearly $1M in revenue its first year, and he projected $2.5 to $3M the next. He shut it down anyway.

The reason: in the age of AI, it was never going to be venture scale. Incumbents could use the same technology to automate tax prep for their existing customers faster than a startup could win market share. Tax is also sold once a year, so he had about a one-month window to pivot before the door closed.

The pivot became Town, an AI assistant that works from your email and calendar. It found product-market fit fast enough that Ben Horowitz messaged a16z's Alex Rampell to say it "changed my life."

In this conversation, Alex and Jean-Denis cover:

- How to know when to kill a business that's working on paper
- Why Town competes with OpenAI and Anthropic, the companies it buys its tokens from
- Why the "forward deployed engineer" model gets worse as AI gets better
- How a $1/month AI routine can save a restaurant thousands
- What agents mean for Amazon, advertising and the future of shopping

0:57 How Ben Horowitz discovered Town
2:20 Why Town started with AI tax prep
3:55 Why tax couldn't be venture scale
6:30 Shutting down a working business
10:24 The customer ask that became Town
13:35 Why Town built for users before IT
16:18 AI's iPhone moment at work
21:28 Jevons paradox for executive assistants
23:13 Town as a "token allocation machine"
28:00 How Town tailors itself to each job
34:03 Competing with your token suppliers
42:24 Staying off the AI labs' radar
44:42 Why AI isn't spreading evenly yet
52:32 How agents break the ad model
55:35 The friction humans actually enjoy
57:05 Why supply aggregators lose to agents

YouTube: youtu.be/o_5f4N6Nq80@jgreze@TownAI@arampell

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬23🔄13❤️119👀33712📊29

⚡ Powered by xgo.ing

Jean-Denis Greze was CTO of Plaid. His next startup, an AI tax product for small businesses, brought...

a16z (@a16z) — Mon, 19 Oct 2026 14:37:04 GMT

Jean-Denis Greze was CTO of Plaid. His next startup, an AI tax product for small businesses, brought in nearly $1M in revenue its first year, and he projected $2.5 to $3M the next. He shut it down anyway.

The reason: in the age of AI, it was never going to be venture scale. Incumbents could use the same technology to automate tax prep for their existing customers faster than a startup could win market share. Tax is also sold once a year, so he had about a one-month window to pivot before the door closed.

The pivot became Town, an AI assistant that works from your email and calendar. It found product-market fit fast enough that Ben Horowitz messaged a16z's Alex Rampell to say it "changed my life."

In this conversation, Alex and Jean-Denis cover:

- How to know when to kill a business that's working on paper
- Why Town competes with OpenAI and Anthropic, the companies it buys its tokens from
- Why the "forward deployed engineer" model gets worse as AI gets better
- How a $1/month AI routine can save a restaurant thousands
- What agents mean for Amazon, advertising and the future of shopping

0:57 How Ben Horowitz discovered Town
2:20 Why Town started with AI tax prep
3:55 Why tax couldn't be venture scale
6:30 Shutting down a working business
10:24 The customer ask that became Town
13:35 Why Town built for users before IT
16:18 AI's iPhone moment at work
21:28 Jevons paradox for executive assistants
23:13 Town as a "token allocation machine"
28:00 How Town tailors itself to each job
34:03 Competing with your token suppliers
42:24 Staying off the AI labs' radar
44:42 Why AI isn't spreading evenly yet
52:32 How agents break the ad model
55:35 The friction humans actually enjoy
57:05 Why supply aggregators lose to agents

YouTube: youtu.be/o_5f4N6Nq80@jgreze@TownAI@arampell

Your browser does not support the video tag.

🔗 View on Twitter

💬42🔄27❤️339👀80854📊124

⚡ Powered by xgo.ing

Mark Pincus, founder of @Zynga, on "proven, better, new," the product framework that was Zynga's rel...

a16z (@a16z) — Sun, 18 Oct 2026 20:30:30 GMT

Mark Pincus, founder of @Zynga, on "proven, better, new," the product framework that was Zynga's religion:

1. Proven: start with what 10/10 existing users of a category already love, and don't change it.

"You do not have the right to change proven. You have not taken enough time to change proven. It is an art that we do not completely understand."

2. Better: only add what 10/10 users agree is better.

"For Zynga Poker, better was no download. That was it. Turns out you lose half your users every time you click download."

3. New: treat anything you think is better as new, and assume it fails.

"Assume all new fails. Write that on the wall... If all new worked, we'd be using new s**t all the time. But how often do you change what's on the front of your iPhone?"

"Users don't care how innovative it is. All they care about is does it feel better, and they can't even tell you why."

@markpinc w/ @JoshLu

Your browser does not support the video tag.

🔗 View on Twitter

speedrun@speedrun

SR008 APPS ARE OPEN. Up to $1M + $10M+ in credits. 12 weeks in SF, Feb through April. Built to help you land capital, customers and talent. Apply by Nov 1: speedrun.a16z.com/apply

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬37🔄44❤️384👀52098📊89

⚡ Powered by xgo.ing

Aaron Levie of @Box on why AI widens the gap between experts and everyone else, and what that means ...

a16z (@a16z) — Sun, 18 Oct 2026 18:30:22 GMT

Aaron Levie of @Box on why AI widens the gap between experts and everyone else, and what that means for college students:

"The biggest gains of AI go to people who have some degree of expertise in an area, to know what is actually true, what is not gonna work, 'What should I integrate from the output of this AI?'"

"If you don't have a deep understanding of your particular space or field or domain, you aren't able to then have the right judgment to make all of those decisions."

"So I think the experts just get more powerful in this world."

"I'm not even convinced that you can tell a college student to learn anything different than any other period in history. Be really good at a particular field, and then AI is merely a turbocharger of your capability in that particular field."

@levie

Your browser does not support the video tag.

🔗 View on Twitter

a16z@a16z

Jad Naous of @grepr_ai on how AI is widening the gap between okay and great engineers:

"Some people were adapting and learning and others weren't as adaptive. And then as we started hiring more people into the team, that disparity between okay engineers and really good engineers just multiplied 10X."

"Ultimately you really have to understand the setup. The more complexity that your brain can handle, the more you can break down the tasks and assign them."

"My team is still small... In my experience, for the things that we do, that is the case because we build distributed systems and distributed algorithms, and your brain has to handle the complexity because AI doesn't do it."

"For the problems that we solve, none of the models can actually think through the things that we do."

@jadtnaous@martin_casado

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬52🔄37❤️261👀35832📊69

⚡ Powered by xgo.ing

Ben Horowitz on the two skills that will matter more than ever before in an AI world: creativity and...

a16z (@a16z) — Sun, 18 Oct 2026 15:30:38 GMT

Ben Horowitz on the two skills that will matter more than ever before in an AI world: creativity and relationships.

"I think those are things that are hard to get out of today's AI in a very good way. So if you don't have instincts around those things, I don't know if all the AI in the world could help you. Whereas the grind-them-out tasks, AI is already really good at."

"Do you have original ideas? Can you establish and maintain very high quality, high fidelity relationships with people off the rip? I think people don't think about that enough."

"One big advantage I think that we have is we really look for that. Many just ignore that whole thing, like it's not a thing... It's a thing."

@bhorowitz w/ @Tocelot

Your browser does not support the video tag.

🔗 View on Twitter

speedrun@speedrun

SR008 APPS ARE OPEN. Up to $1M + $10M+ in credits. 12 weeks in SF, Feb through April. Built to help you land capital, customers and talent. Apply by Nov 1: speedrun.a16z.com/apply

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬54🔄65❤️580👀66930📊118

⚡ Powered by xgo.ing

Ben Horowitz on what it would take to actually see an AI "fast takeoff" scenario, using a framework ...

a16z (@a16z) — Sat, 17 Oct 2026 20:45:08 GMT

Ben Horowitz on what it would take to actually see an AI "fast takeoff" scenario, using a framework from Databricks CEO @alighodsi:

1. Each new model needs far fewer GPUs than the last
2. Each new model takes less time to train
3. Each new model is more accurate than the last
4. It keeps happening, again and again

"Zero of those four have been met... We're really talking about a risk that's entirely theoretical at this point."

@bhorowitz on @theallinpod

Your browser does not support the video tag.

🔗 View on Twitter

a16z@a16z

Ali Ghodsi of @Databricks on the four conditions that would have to hold for AI to truly improve itself:

"The next model requires less resources, less GPUs to train... The second condition: the next model takes less time to train as well. Third, accuracy of the model, the intelligence is increasing. And fourth, we can do the former three again and again and again... All at the same time, not just any of them."

"If you just mean that the software is writing itself, we're already there today. Like 90-some percent of the software in Databricks is written by AI. Does it matter if the last few percent is also written by AI? No."

"If you're getting these four conditions, then you might get a speed-up where the next model, let's say, takes half amount of time and half the resources, and it is more intelligent... By the way, I don't even know if that necessarily leads you to superintelligence."

"A lot of people, their definition is just, 'Hey, if I'm not even coding anymore and it's coding itself'... I think they're conflating, 'Hey, what's my value and is it scary for me?' Versus, 'Hey, that then means we'll get that superintelligence that in 2014 was hypothesized by Bostrom.'"

@alighodsi@martin_casado@sarah_wang

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬42🔄86❤️674👀70120📊115

⚡ Powered by xgo.ing

Gavin Baker on the other side of Ben's bandwidth story: at the peak of the 2000 bubble, 97% of Ameri...

a16z (@a16z) — Sat, 17 Oct 2026 19:15:25 GMT

Gavin Baker on the other side of Ben's bandwidth story: at the peak of the 2000 bubble, 97% of America's fiber sat dark, and today there are no dark GPUs.

"I had, depending on how you look at it, the privilege and the misfortune of being a tech investor during the year 2000 bubble, which was really a telecom bubble."

"The year 2000 bubble was defined by something called 'dark fiber.' Dark fiber was literally fiber that was laid down in the ground, and not lit up... At the peak of the bubble, 97% of the fiber that had been laid in America was dark."

"Contrast that with today. There are no dark GPUs. All you have to do is read any technical paper, and one of the biggest problems in a training run is that GPUs are melting."

@GavinSBaker

Your browser does not support the video tag.

🔗 View on Twitter

a16z@a16z

Ben Horowitz on the lesson LoudCloud, his early cloud company, taught him about today's AI neoclouds, and why they now face the opposite problem:

"It can end up being like a very dangerous arbitrage business, where you can get upside-down easily. And so if you look at LoudCloud, just as an example, here's a simple thing that happened to us."

"In 1999, bandwidth was $1,500 a megabit. And you couldn't get it. It was very hard to get it. And so you had to get a long-term contract, three years. By 2001, it was $50 a megabit... And if you borrowed money to get it, you're dead."

"That kind of thing is so out of the control of the entrepreneur."

"I think the core dynamic of AI has been working for CoreWeave, though, in that there's a massive shortage, not an overage of supply... Token prices have gone down, but GPU prices have not."

"And by the way, data center space is going up in price because even labor to build data centers is hard. The politics are making it hard to build data centers. So the whole supply chain price is going up and demand is not subsiding."

"Those are very big tailwinds for CoreWeave, despite the fact that the debt markets, kind of independent of them, are tough."

@bhorowitz w/ @chamath on @allinpod

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬61🔄77❤️702👀185737📊124

⚡ Powered by xgo.ing

AI is helping Americans shop, but 42% don't trust it to make a purchase yet Even in the most popula...

a16z (@a16z) — Sat, 17 Oct 2026 18:00:11 GMT

AI is helping Americans shop, but 42% don't trust it to make a purchase yet

Even in the most popular categories, only about a quarter would let AI choose and buy for them:

- 27% for clothing
- 26% for electronics
- 25% for travel

Charts of the Week: a16z.news/p/this-week-in…

a16z@a16z

More than 1 in 3 Americans bought something based on an AI recommendation in the past year

44% say AI helps them find better value for money

34% would pay more for a product if AI showed it was the best option for them

Charts of the Week: a16z.news/p/this-week-in…

🔗 View Quoted Tweet

💬60🔄29❤️253👀34362📊67

⚡ Powered by xgo.ing

Ali Ghodsi of @Databricks on the four conditions that would have to hold for AI to truly improve its...

a16z (@a16z) — Sat, 17 Oct 2026 16:50:02 GMT

Ali Ghodsi of @Databricks on the four conditions that would have to hold for AI to truly improve itself:

"The next model requires less resources, less GPUs to train... The second condition: the next model takes less time to train as well. Third, accuracy of the model, the intelligence is increasing. And fourth, we can do the former three again and again and again... All at the same time, not just any of them."

"If you just mean that the software is writing itself, we're already there today. Like 90-some percent of the software in Databricks is written by AI. Does it matter if the last few percent is also written by AI? No."

"If you're getting these four conditions, then you might get a speed-up where the next model, let's say, takes half amount of time and half the resources, and it is more intelligent... By the way, I don't even know if that necessarily leads you to superintelligence."

"A lot of people, their definition is just, 'Hey, if I'm not even coding anymore and it's coding itself'... I think they're conflating, 'Hey, what's my value and is it scary for me?' Versus, 'Hey, that then means we'll get that superintelligence that in 2014 was hypothesized by Bostrom.'"

@alighodsi@martin_casado@sarah_wang

Your browser does not support the video tag.

🔗 View on Twitter

💬33🔄17❤️216👀93148📊57

⚡ Powered by xgo.ing

Ben Horowitz on the lesson LoudCloud, his early cloud company, taught him about today's AI neoclouds...

a16z (@a16z) — Sat, 17 Oct 2026 15:03:53 GMT

Ben Horowitz on the lesson LoudCloud, his early cloud company, taught him about today's AI neoclouds, and why they now face the opposite problem:

"It can end up being like a very dangerous arbitrage business, where you can get upside-down easily. And so if you look at LoudCloud, just as an example, here's a simple thing that happened to us."

"In 1999, bandwidth was $1,500 a megabit. And you couldn't get it. It was very hard to get it. And so you had to get a long-term contract, three years. By 2001, it was $50 a megabit... And if you borrowed money to get it, you're dead."

"That kind of thing is so out of the control of the entrepreneur."

"I think the core dynamic of AI has been working for CoreWeave, though, in that there's a massive shortage, not an overage of supply... Token prices have gone down, but GPU prices have not."

"And by the way, data center space is going up in price because even labor to build data centers is hard. The politics are making it hard to build data centers. So the whole supply chain price is going up and demand is not subsiding."

"Those are very big tailwinds for CoreWeave, despite the fact that the debt markets, kind of independent of them, are tough."

@bhorowitz w/ @chamath on @allinpod

Your browser does not support the video tag.

🔗 View on Twitter

💬32🔄22❤️258👀183253📊58

⚡ Powered by xgo.ing

More than 1 in 3 Americans bought something based on an AI recommendation in the past year 44% say ...

a16z (@a16z) — Fri, 16 Oct 2026 20:45:02 GMT

More than 1 in 3 Americans bought something based on an AI recommendation in the past year

44% say AI helps them find better value for money

34% would pay more for a product if AI showed it was the best option for them

Charts of the Week: a16z.news/p/this-week-in…

💬61🔄59❤️412👀63608📊117

⚡ Powered by xgo.ing

Will O'Brien of @UlyssesInc on why today's young founders are more Steve Jobs than Jeff Bezos: "The...

a16z (@a16z) — Fri, 16 Oct 2026 19:40:10 GMT

Will O'Brien of @UlyssesInc on why today's young founders are more Steve Jobs than Jeff Bezos:

"The founders look very different, right? It's like the kind of rah-rah, smoking, go to church on Sunday, maybe a bit louder, working typically on hardware rather than software."

"It's like a different motivation. There is seemingly more of a kind of a spiritual reason to why people start their businesses."

"I often think of there being head and heart type founders, where Jeff Bezos is the canonical head-based founder... 'What would be the best thing to sell on the internet? Oh, books are good because you can move them easily.'"

"Steve Jobs being the canonical heart-based founder, the bicycle for the mind, the hippie on acid... And I think this era of founders are way more oriented on the heart-based."

"A lot of us were very, I think, impacted by something like COVID. A lot of us are typically in our later 20s now, but were in college or something like that when it happened. It kind of made me feel a little bit cheated."

"We were forced to be in the digital world, so we feel cause to impact the physical world... Or maybe we're just more of a product of the times that we're in."

@Willob on Cheeky Pint

Your browser does not support the video tag.

🔗 View on Twitter

💬13🔄11❤️314👀80799📊49

⚡ Powered by xgo.ing

Jad Naous of @grepr_ai asks a16z's Martin Casado if AI can do a VC's job: Martin: "My model for VC ...

a16z (@a16z) — Fri, 16 Oct 2026 18:45:36 GMT

Jad Naous of @grepr_ai asks a16z's Martin Casado if AI can do a VC's job:

Martin: "My model for VC is it's very relationship driven. I just have a belief that markets are actually pretty efficient. It's not about finding the company that nobody else finds. I think people all find the right company."

"I think the key is access. It's raising money, it's ability to win deals, and it's ability to add value after that happens."

"A lot of that is stuff that startups just can't naturally do. So the way that I think about venture, you kind of have to be a human being to do, because that's the actual job."

"That said, I will say I have seen an analysis done by AI that are as good or better than any genius I've ever worked with. So for the analysis piece, they're great."

"But I think this is more an indictment on VCs' ability to analyze than it is how powerful the models are. I just think it's all a kabuki show and bullshit."

"The markets are very, very efficient. I just feel like my answer is almost a technicality."

@jadtnaous@martin_casado

Your browser does not support the video tag.

🔗 View on Twitter

a16z@a16z

Engineering leaders told Grepr founder and CEO Jad Naous they spend 5 to 20% of their infrastructure budget on observability, and it's buying less and less reliability.

Jad, a former a16z partner, joins a16z's Martin Casado to discuss whether AI can run production systems as well as it writes code.

Grepr sits in a company's log stream, can scan a petabyte of logs a day, cuts observability costs by about 90%, and is building AI agents that use those signals to troubleshoot production issues.

How AI is changing engineering at Jad's 10-person company:
- A data lake feature that once took one engineer about six months took Jad 10 days
- The gap between okay and great engineers on his team "just multiplied 10X"
- He runs 3-4 agent sessions at once, with a "tech lead" agent reviewing every PR

Why running live systems is a harder problem for AI:
- Jad: AI can almost tune databases, but can't yet keep servers running on its own
- Martin: every bit of code doubles the ways a program can behave, which is why humans still own systems
- The real world is messier than AI's training data, self-driving took 20 years and $170B

0:55 From Stanford to a16z to Grepr
7:00 Why observability spend stopped paying off
11:00 From autocomplete to AI that trains engineers
13:10 Why AI widens the engineer skill gap
14:55 Syntax vs semantics
18:00 Inside Jad's AI workflow
22:55 Six months of work in 10 days
24:50 Can AI reason about how systems really run?
30:10 Why AI SRE tools start in the wrong place
32:30 Can AI run a distributed cluster yet?
37:35 Why engineers buy extra capacity over predicting
39:15 How AI could trace failures in production
43:45 Why AI for ops is harder than for code
49:55 Can AI do a VC's job?
52:05 Why self-driving took 20 years and $170B
57:05 Customers skipped the AI pitch for the 90% savings

YouTube: youtu.be/blkIGLlZePg@jadtnaous@grepr_ai@martin_casado

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬15🔄7❤️88👀27231📊18

⚡ Powered by xgo.ing

Narrative violation: Gen Z is out-earning what millennials made at their age Adjusted for inflation...

a16z (@a16z) — Fri, 16 Oct 2026 17:45:02 GMT

Narrative violation: Gen Z is out-earning what millennials made at their age

Adjusted for inflation, zoomers earn 20-40% more than millennials did a decade ago, at every income level

Charts of the Week: a16z.news/p/this-week-in…

💬59🔄44❤️517👀58261📊112

⚡ Powered by xgo.ing

Jad Naous of @grepr_ai on how AI is widening the gap between okay and great engineers: "Some people...

a16z (@a16z) — Fri, 16 Oct 2026 16:47:44 GMT

Jad Naous of @grepr_ai on how AI is widening the gap between okay and great engineers:

"Some people were adapting and learning and others weren't as adaptive. And then as we started hiring more people into the team, that disparity between okay engineers and really good engineers just multiplied 10X."

"Ultimately you really have to understand the setup. The more complexity that your brain can handle, the more you can break down the tasks and assign them."

"My team is still small... In my experience, for the things that we do, that is the case because we build distributed systems and distributed algorithms, and your brain has to handle the complexity because AI doesn't do it."

"For the problems that we solve, none of the models can actually think through the things that we do."

@jadtnaous@martin_casado

Your browser does not support the video tag.

🔗 View on Twitter

💬15🔄6❤️59👀35775📊19

⚡ Powered by xgo.ing

In 2022, Berkeley PhD student Woosuk Kwon thought he could make Meta's new open-weight model, OPT-17...

a16z (@a16z) — Fri, 16 Oct 2026 15:47:29 GMT

In 2022, Berkeley PhD student Woosuk Kwon thought he could make Meta's new open-weight model, OPT-175B, run faster in two weeks.

Four years and 3,000 contributors later, that project is @vllm_project, one of the largest open-source inference engines, running on ~500,000 GPUs at any given time.

He and fellow researchers Simon Mo, Kaichao You, and Roger Wang borrowed "virtual memory" from operating systems, handing out memory only as it's needed so each GPU can juggle requests of wildly different sizes. (The "v" in vLLM is for virtual.)

Their first user, Berkeley neighbor @arena, cut its GPU needs in half. Today Google, Amazon, and Meta all run vLLM in production.

In January, the core maintainers founded @inferact and raised $150M to give vLLM a full-time team.

They're hiring across eng, marketing, and devrel in SF and Singapore: a16zjobs.substack.com/p/inferact-rai…@simon_mo_@woosuk_k@KaichaoYou@rogerw0108

Your browser does not support the video tag.

🔗 View on Twitter

💬30🔄49❤️533👀69533📊104

⚡ Powered by xgo.ing

Engineering leaders told Grepr founder and CEO Jad Naous they spend 5 to 20% of their infrastructure...

a16z (@a16z) — Fri, 16 Oct 2026 14:53:08 GMT

Engineering leaders told Grepr founder and CEO Jad Naous they spend 5 to 20% of their infrastructure budget on observability, and it's buying less and less reliability.

Jad, a former a16z partner, joins a16z's Martin Casado to discuss whether AI can run production systems as well as it writes code.

Grepr sits in a company's log stream, can scan a petabyte of logs a day, cuts observability costs by about 90%, and is building AI agents that use those signals to troubleshoot production issues.

How AI is changing engineering at Jad's 10-person company:
- A data lake feature that once took one engineer about six months took Jad 10 days
- The gap between okay and great engineers on his team "just multiplied 10X"
- He runs 3-4 agent sessions at once, with a "tech lead" agent reviewing every PR

Why running live systems is a harder problem for AI:
- Jad: AI can almost tune databases, but can't yet keep servers running on its own
- Martin: every bit of code doubles the ways a program can behave, which is why humans still own systems
- The real world is messier than AI's training data, self-driving took 20 years and $170B

0:55 From Stanford to a16z to Grepr
7:00 Why observability spend stopped paying off
11:00 From autocomplete to AI that trains engineers
13:10 Why AI widens the engineer skill gap
14:55 Syntax vs semantics
18:00 Inside Jad's AI workflow
22:55 Six months of work in 10 days
24:50 Can AI reason about how systems really run?
30:10 Why AI SRE tools start in the wrong place
32:30 Can AI run a distributed cluster yet?
37:35 Why engineers buy extra capacity over predicting
39:15 How AI could trace failures in production
43:45 Why AI for ops is harder than for code
49:55 Can AI do a VC's job?
52:05 Why self-driving took 20 years and $170B
57:05 Customers skipped the AI pitch for the 90% savings

YouTube: youtu.be/blkIGLlZePg@jadtnaous@grepr_ai@martin_casado

Your browser does not support the video tag.

🔗 View on Twitter

💬29🔄11❤️99👀52220📊38

⚡ Powered by xgo.ing

Aaron Levie of @Box on how AI agents with wallets could create new business models for the internet:...

a16z (@a16z) — Thu, 15 Oct 2026 20:41:05 GMT

Aaron Levie of @Box on how AI agents with wallets could create new business models for the internet:

"I've always wanted micropayments for things online... I've appreciated why it's a bad business model, but I want it."

"Now it's probably a good business model. Let's say you had paywalled, really proprietary data. You're Bloomberg. You're a medical institution. You could imagine there's maybe a hundred million agents that would be tapping into that data."

"All of a sudden, a penny a transaction starts to make a lot more sense than it did when you were capped at 20 million [knowledge workers] that maybe were interested in your content."

"We probably have new business models for tools and APIs... We probably want an agent to be able to have a budget and make any API call it wants."

"All of a sudden you have this new revenue stream, which is these agents showing up, just transacting on your system."

@levie at Daytona Compute 2026

Your browser does not support the video tag.

🔗 View on Twitter

Terry Angelos@terryangelos

Wallet Wars: Agents need payment credentials as they become the new UX for $7T in ecommerce. Enter the Wallet Wars and why the AOL-era of agentic commerce will give way to open standards. x.com/i/article/2110…

🔗 View Quoted Tweet

💬39🔄32❤️209👀35501📊54

⚡ Powered by xgo.ing

How your AI agent can pay with your credit card, without ever holding it: 1. Your card stays stored...

a16z (@a16z) — Thu, 15 Oct 2026 19:00:22 GMT

How your AI agent can pay with your credit card, without ever holding it:

1. Your card stays stored in a trusted wallet.

2. When your agent goes to buy something, the wallet creates an agentic token: a stand-in card number that only works at that store, with limits like "don't spend more than $100."

3. Your agent hands the token to the store.

4. The store runs it through its usual payment processor, and the charge lands on your real card, with your rewards and card benefits intact.

"AOL was a helpful set of training wheels for getting everyone online, but the open web ultimately won. We think the same thing will happen with agents as well."

Full piece from VGS's @terryangelos on the Wallet Wars: a16z.news/p/wallet-wars

Terry Angelos@terryangelos

Wallet Wars: Agents need payment credentials as they become the new UX for $7T in ecommerce. Enter the Wallet Wars and why the AOL-era of agentic commerce will give way to open standards. x.com/i/article/2110…

🔗 View Quoted Tweet

💬47🔄45❤️337👀38940📊92

⚡ Powered by xgo.ing

VGS's Terry Angelos and a16z's Alex Rampell on the one thing AI agent payments can't afford to break...

a16z (@a16z) — Thu, 15 Oct 2026 18:00:32 GMT

VGS's Terry Angelos and a16z's Alex Rampell on the one thing AI agent payments can't afford to break: the right to get your money back.

Terry: "I asked Instinct to go and help me find these lights, and it found the lights at Walmart... when it came time to pay, Link [my payment wallet] didn't have a connection to Walmart. It can't just give my card over to the agent and over to Walmart."

"If there's a problem... I call up my bank... And they say, 'Well, we don't see a Walmart transaction here... We just see a [Link] transaction.'"

Alex: "One of the reasons why Visa and Mastercard have such impregnable positions is because they intermediate everything, and they set the rules, and chargebacks are very important."

"It's a trust mechanism. I pay you for lights. You don't deliver the lights... You actually have rules by which I can kind of lodge a complaint."

"That's high utility, actually even for the merchant... I'm much more willing to buy with a merchant knowing that if they don't actually deliver a product that works, I can get my money back."

@terryangelos@arampell

Your browser does not support the video tag.

🔗 View on Twitter

a16z@a16z

The wallet wars are here.

Very Good Security chief strategy officer Terry Angelos joins a16z's Alex Rampell, his TrialPay co-founder from back in 2006 (TrialPay was later acquired by Visa), to discuss how agents are learning to spend money.

"Agents have all of a sudden become significantly more helpful and useful because they effectively have arms and legs, which are payment credentials."

VGS holds the payment credentials behind about 1/3 of all e-commerce, and now it's helping AI agents pay merchants. If you've bought anything online, there's a good chance VGS has already helped keep your card safe.

In the last three months, Terry says agents went from "spectators to actors." Once an agent has your payment credentials, it can book the piano teacher it previously could only find and recommend.

Now Stripe's Link, Shopify's Shop Pay, and PayPal are racing to connect to agents. It's all headed toward open standards from the card networks: agentic tokens that let an agent pay with your actual card.

VGS went from zero agentic tokens to a few million a week in three months.

0:55 From TrialPay to Visa to VGS
2:05 How VGS vaults 1/3 of e-commerce
6:05 Why big merchants use 3-5 processors
9:55 Why AI agents need a payment vault
12:15 Agents go from "spectators to actors"
13:40 The wallet wars, old and new
19:55 Walled gardens vs open standards
23:55 How agents can pay with your real card
26:40 Your saved card goes multiplayer
29:40 How do you trust an agent's purchase?
32:35 Who pays when your agent gets it wrong
35:40 Why Amazon blocks agents
39:50 What's overhyped and underhyped
43:35 When merchants get agents too
45:40 Why agents could break card rewards
47:00 VGS's eras: vault, updates, network

@terryangelos@arampell

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬16🔄5❤️53👀17533📊15

⚡ Powered by xgo.ing

AI agents can now book the hotel and order the cleats, as long as they're allowed to pay. That's se...

a16z (@a16z) — Thu, 15 Oct 2026 17:00:33 GMT

AI agents can now book the hotel and order the cleats, as long as they're allowed to pay.

That's set off the "wallet wars," with major players racing to connect to agents:

- Stripe's Link: 300M users
- Shopify's Shop Pay: 250M users
- PayPal: 400M+ users

How your agent pays matters. If a wallet pays the store with its own stand-in card, the purchase goes through, but your card rewards, purchase protections, and relationship with the store can get lost along the way.

Say your agent books a United flight on your airline credit card. If the wallet pays United with its own virtual card, the money moves, and your miles may never show up.

Agents also have to pick the right card for each purchase, and when one books the wrong hotel dates, someone has to make it right.

Where it's all headed: new open standards from Visa, Mastercard, and Amex.

"The best of all worlds is a future where my agent can use my credit card, without having my credit card."

Full piece from VGS's @terryangelos on the Wallet Wars: a16z.news/p/wallet-wars

Terry Angelos@terryangelos

Wallet Wars: Agents need payment credentials as they become the new UX for $7T in ecommerce. Enter the Wallet Wars and why the AOL-era of agentic commerce will give way to open standards. x.com/i/article/2110…

🔗 View Quoted Tweet

💬57🔄34❤️316👀38574📊83

⚡ Powered by xgo.ing

The wallet wars are here. Very Good Security chief strategy officer Terry Angelos joins a16z's Alex...

a16z (@a16z) — Thu, 15 Oct 2026 15:54:55 GMT

The wallet wars are here.

Very Good Security chief strategy officer Terry Angelos joins a16z's Alex Rampell, his TrialPay co-founder from back in 2006 (TrialPay was later acquired by Visa), to discuss how agents are learning to spend money.

"Agents have all of a sudden become significantly more helpful and useful because they effectively have arms and legs, which are payment credentials."

VGS holds the payment credentials behind about 1/3 of all e-commerce, and now it's helping AI agents pay merchants. If you've bought anything online, there's a good chance VGS has already helped keep your card safe.

In the last three months, Terry says agents went from "spectators to actors." Once an agent has your payment credentials, it can book the piano teacher it previously could only find and recommend.

Now Stripe's Link, Shopify's Shop Pay, and PayPal are racing to connect to agents. It's all headed toward open standards from the card networks: agentic tokens that let an agent pay with your actual card.

VGS went from zero agentic tokens to a few million a week in three months.

0:55 From TrialPay to Visa to VGS
2:05 How VGS vaults 1/3 of e-commerce
6:05 Why big merchants use 3-5 processors
9:55 Why AI agents need a payment vault
12:15 Agents go from "spectators to actors"
13:40 The wallet wars, old and new
19:55 Walled gardens vs open standards
23:55 How agents can pay with your real card
26:40 Your saved card goes multiplayer
29:40 How do you trust an agent's purchase?
32:35 Who pays when your agent gets it wrong
35:40 Why Amazon blocks agents
39:50 What's overhyped and underhyped
43:35 When merchants get agents too
45:40 Why agents could break card rewards
47:00 VGS's eras: vault, updates, network

@terryangelos@arampell

Your browser does not support the video tag.

🔗 View on Twitter

Terry Angelos@terryangelos

Wallet Wars: Agents need payment credentials as they become the new UX for $7T in ecommerce. Enter the Wallet Wars and why the AOL-era of agentic commerce will give way to open standards. x.com/i/article/2110…

🔗 View Quoted Tweet

💬24🔄12❤️178👀56385📊47

⚡ Powered by xgo.ing

The full conversation with @AnneNeuberger, @jai_ramaswamy, and @GoldsmithOnGov on SAIF: https://t.co...

a16z (@a16z) — Thu, 15 Oct 2026 15:26:28 GMT

The full conversation with @AnneNeuberger, @jai_ramaswamy, and @GoldsmithOnGov on SAIF: a16zpolicy.substack.com/p/securing-ame…

💬0🔄0❤️4👀3260📊1

⚡ Powered by xgo.ing

AI could help defend communities that lack the resources to fight off cyberattacks. a16z's @AnneNeu...

a16z (@a16z) — Thu, 15 Oct 2026 15:24:27 GMT

AI could help defend communities that lack the resources to fight off cyberattacks.

a16z's @AnneNeuberger on the mission behind the Securing America's Infrastructure Foundation, starting with rural hospitals:

"America has 1,800 rural hospitals... Those hospitals lack the skills, the people, the money to ensure that they are not disrupted due to cyberattacks."

"AI makes a real difference in being able to economically defend networks, to find vulnerabilities..."

"A, can we build an easy open-source AI harness to enable us to find and block cyberattacks?"

"B, stand up an effort to take the code on IoT devices like internet-connected MRI machines to patch those."

"And thirdly, when there is an incident, adopt the model that New York City and New York State used, where they set up a security operations center that was able to support multiple smaller communities in responding to an incident."

"So those are the three parts we're thinking about in helping critical infrastructure leverage AI to secure the critical systems Americans rely on every day."

Full announcement introducing the Securing America's Infrastructure Foundation: a16z.news/p/introducing-…

Your browser does not support the video tag.

🔗 View on Twitter

Anne Neuberger@AnneNeuberger

x.com/i/article/2110…

🔗 View Quoted Tweet

💬7🔄15❤️67👀17790📊12

⚡ Powered by xgo.ing

Northwood Space CEO Bridgit Mendler on building the infrastructure for 1,000 SpaceXs: "I wanna see ...

a16z (@a16z) — Wed, 14 Oct 2026 22:58:35 GMT

Northwood Space CEO Bridgit Mendler on building the infrastructure for 1,000 SpaceXs:

"I wanna see 1,000 SpaceXs. And I wanna see them not take 25 years to get there. I wanna see them get there in five years."

"I really believe that the best outcome for humanity is being able to have a real, open, unfettered, democratized space future."

"Being able to build infrastructure that introduces capabilities that so many different, not just companies, but countries can stand on, is really exciting."

"Think of cloud infrastructure and what it enabled for all of the internet-based companies. So many companies would not have been able to achieve that global reach if they had to think about deploying their own fiber and standing up internet exchange points all around the world."

"I know it sounds like a ludicrous thing to say, 1,000 SpaceXs, but if you said that about cloud, maybe it doesn't sound so ridiculous now."

@bridgitmendler w/ @itsmoislam

Your browser does not support the video tag.

🔗 View on Twitter

💬28🔄39❤️472👀47756📊81

⚡ Powered by xgo.ing

Compute scarcity, in one chart Old GPUs are turning into appreciating assets

a16z (@a16z) — Wed, 14 Oct 2026 20:30:02 GMT

Compute scarcity, in one chart

Old GPUs are turning into appreciating assets

💬68🔄108❤️1009👀117847📊197

⚡ Powered by xgo.ing

Five bangers from Nathan Lambert: 1. China's compute gap: "You talk to people [in China], and you'...

a16z (@a16z) — Wed, 14 Oct 2026 19:00:13 GMT

Five bangers from Nathan Lambert:

1. China's compute gap:

"You talk to people [in China], and you're like, 'Oh, I heard an average OpenAI researcher has five thousand H100 equivalents that they can access at any time,' and like their jaw just falls to the floor. They're just like, 'I can't comprehend.'"

2. China's talent pool:

"The Chinese labs are very technically excellent... To say that China is anything but equal on talent, you're gonna have a hard time because they just have so many people being trained at such a high level."

3. OpenAI's superpower:

"[The frontier labs] have just taken the most ambitious bets the earliest in order to get there, and that's why OpenAI has done things like kickstart the reasoning model revolution."

"And I think that kind of breakthrough and that beautiful plot with the inference scaling laws is peak OpenAI. They're really good at getting this plot that just changes how everyone thinks about everything."

4. Underestimating the competition:

"The Chinese are so good at building models as well. I think there's a bit of cope where people are like, 'Only we are good at building models.' There are so many companies. There's, like, 20 to 30 companies that are building good models."

5. The AI risks that are already here:

"[The lack of preparation] is kind of the thing that worries me more than, say, RSI is coming and unlocking new risks. There are risks that are here and that are gonna continue to diffuse."

"Figuring out how to get society to prepare is very hard. I'm of the opinion that we need more eyes, we need more science, so that society feels informed."

@natolambert

💬15🔄28❤️284👀42673📊64

⚡ Powered by xgo.ing

Now that AI agents can own an outcome from start to finish, how do you sell them? 1. Sell the platf...

a16z (@a16z) — Wed, 14 Oct 2026 18:00:16 GMT

Now that AI agents can own an outcome from start to finish, how do you sell them?

1. Sell the platform:

License the agent to a company to run in its own operations. No existing contract sets the price, so there's more upside, but new budgets take time to approve and results are harder to credit to the agent.

2. Sell the service:

Take over work a company already outsources, the way Red Ventures sold policies for insurers and got paid per policy closed. The budget already exists, so deals close faster, but the highest-value work usually runs in-house, which caps the upside.

3. Go full-stack:

Become the business, like buying debt portfolios and collecting on them yourself. You keep all the value the AI creates, but you also take on the licensing and compliance.

Full piece from a16z's @dhaber on long-running agents in high-value customer journeys: a16z.news/p/winning-the-…

David Haber@dhaber

x.com/i/article/2110…

🔗 View Quoted Tweet

💬71🔄64❤️844👀97178📊256

⚡ Powered by xgo.ing

Nathan Lambert of @trillium_labs says Chinese AI researchers' jaws drop to the floor when he tells t...

a16z (@a16z) — Wed, 14 Oct 2026 17:00:46 GMT

Nathan Lambert of @trillium_labs says Chinese AI researchers' jaws drop to the floor when he tells them how much compute OpenAI has:

"The Chinese labs are very technically excellent. They are more compute-starved."

"Without a doubt, they have a comparable talent pool to draw on... To say that China is anything but equal on talent, you're gonna have a hard time because they just have so many people being trained at such a high level."

"What they don't have is compute."

"You talk to people there, and you're like, 'Oh, I heard an average OpenAI researcher has five thousand H100 equivalents that they can access at any time,' and like their jaw just falls to the floor. They're just like, 'I can't comprehend.'"

@natolambert@lishali88

Your browser does not support the video tag.

🔗 View on Twitter

💬32🔄51❤️419👀182555📊89

⚡ Powered by xgo.ing

How AI for the enterprise is evolving, from owning a task to owning an outcome: 1. Assistant: handl...

a16z (@a16z) — Wed, 14 Oct 2026 16:00:24 GMT

How AI for the enterprise is evolving, from owning a task to owning an outcome:

1. Assistant: handles one task in seconds, like sending a follow-up email. Companies pay for it out of their software budget.

2. Agent: handles one interaction in minutes, like a customer's refund call. It takes on work people used to do, so it gets paid for out of the labor budget.

3. Long-running agent: works toward a goal for weeks or months, like getting an injured client the biggest settlement possible. Because it owns the result, the company selling it can price on the outcome it delivers.

Full piece from a16z's @dhaber on long-running agents in high-value customer journeys: a16z.news/p/winning-the-…

David Haber@dhaber

x.com/i/article/2110…

🔗 View Quoted Tweet

💬52🔄98❤️606👀47732📊153

⚡ Powered by xgo.ing

Nathan Lambert, former post-training co-lead at the Allen Institute for AI, now launching Trillium, ...

a16z (@a16z) — Wed, 14 Oct 2026 15:00:16 GMT

Nathan Lambert, former post-training co-lead at the Allen Institute for AI, now launching Trillium, a nonprofit open AI lab, joins a16z's Lisha Li to discuss whether open models can really keep up with the frontier.

When he tells researchers in China that an average OpenAI researcher reportedly has five thousand H100s to work with, "their jaw just falls to the floor." In his view, Chinese labs have the talent but are mostly short on compute.

He also thinks the distillation story is overblown. Based on the research, he puts its edge at a few months, and says "there's a bit of cope" in thinking only American labs can build good models.

A lot of frontier progress outside math and code comes down to labs buying 1) data and 2) RL environments. It's a multibillion-dollar market, and some of it is sloppy enough to teach models to reward hack.

That's a big part of why Trillium exists. Nathan wants to give away the environments other people would sell, and show exactly how his team uses AI to build AI, including where the models still get stuck. His bet is that the more people can see how this works, the better prepared everyone is for what's coming.

55 Can open models keep up with the frontier?
05 What it takes to reach the frontier
55 Open weights and the fast-follower playbook
25 Chinese labs: equal talent, less compute
55 Why distillation is harder than it looks
55 Is distillation theft or competition?
20 The lab flywheel vs owning your weights
45 Can general models beat every specialist?
30 AI at 1% of today's cost in 4 years
15 The RL env boom and reward hacking
30 What Trillium will build in the open
30 Breaking into AI in the age of AI slop
05 Open-sourcing how AI builds AI
25 Why Nathan isn't pre-training
45 Agent swarms and why lab secrets leak
25 Fast takeoff vs risks already here

YouTube: youtu.be/BKuK0M6oDTI@natolambert@trillium_labs@lishali88

Your browser does not support the video tag.

🔗 View on Twitter

💬17🔄25❤️197👀137802📊58

⚡ Powered by xgo.ing

Until recently, AI agents mostly handled short tasks, like one support call or one email. Now they c...

a16z (@a16z) — Wed, 14 Oct 2026 14:30:24 GMT

Until recently, AI agents mostly handled short tasks, like one support call or one email. Now they can stay on a job for weeks or months.

That matters for "relay race" work that runs through a long chain of people.

Every handoff in that chain is a chance for something to fall through: a slow callback, a dropped follow-up, lost context.

Take a personal injury case. Today it moves from intake to investigation to the demand letter, with different people at each step. A single case agent can work it from the first call to settlement, chasing witnesses, records, and bills, and keeping the client and attorney updated along the way.

The same idea applies to processes like mortgages, insurance claims, and enterprise sales.

Because the agent owns the end result, the company selling it can charge on the outcome (a closed loan, a settled case, a signed deal) and move past pricing on the labor it saves (paralegal hours, call center seats).

Full piece from a16z's @dhaber on long-running agents in high-value customer journeys: a16z.news/p/winning-the-…

David Haber@dhaber

x.com/i/article/2110…

🔗 View Quoted Tweet

💬52🔄29❤️309👀36206📊84

⚡ Powered by xgo.ing

a16z crypto's Eddy Lazzarin on why AI makes the world more secure: bugs are finite, and the cost of ...

a16z (@a16z) — Tue, 13 Oct 2026 22:55:27 GMT

a16z crypto's Eddy Lazzarin on why AI makes the world more secure: bugs are finite, and the cost of finding them keeps collapsing.

"Making comparisons between models from a year ago versus today... Some people were coming up with estimates like it's... 99% cheaper for intellectual capability per dollar."

"Pair that with a frame we've talked about before, which is that the number of bugs in software is finite. Then we're asymptotically approaching zero bugs. Maybe we actually just hit zero at some point."

"If that's the case, we are net improving the security status of the world instead of eroding it."

"This is not a battle of wits where you have a system, I have a system, your system is smarter than mine, therefore I am vulnerable to you... That relies on there being vulnerabilities, and vulnerabilities can be removed."

"The primary resource we dedicate to mitigating it is a thing that's getting radically cheaper every couple months. That's AI."

Dan Boneh: "Companies actually are already doing this. They're spending a lot of money on scanning their code base with the latest models... Every time there's a software update... Every time a new model comes out, they scan the software again."

@eddylazzarin@danboneh

Your browser does not support the video tag.

🔗 View on Twitter

a16z@a16z

AI deflated as much in 3 years as PCs did in 15

Cheap intelligence is what lets a model move out of the chatbot and into the software where the work happens

🔗 View Quoted Tweet

💬19🔄6❤️82👀38738📊19

⚡ Powered by xgo.ing

a16z crypto's Eddy Lazzarin and Stanford's Dan Boneh on how AI's math leap could turn a decade of tr...

a16z (@a16z) — Tue, 13 Oct 2026 21:01:27 GMT

a16z crypto's Eddy Lazzarin and Stanford's Dan Boneh on how AI's math leap could turn a decade of trust-building in cryptography into one month:

Eddy: "We specifically rely in some sense on time... We put these schemes out and we wait. And we wait because what we believe is happening is that experts, like cryptographers and mathematicians, are testing and trying to break these things."

"Because of the pace of progress on AI's mathematical abilities, there is a conspicuous absence of time... A whole new well of cognitive ability to scrutinize has appeared."

Dan: "In the past, when someone would make an assumption and build a system based on it, we'd have to wait like 10 years before that system could be deployed."

"Now we can just let a model run for a month, try to break it, and if the model can't break it, boy, do we have a lot of confidence that the assumption is valid."

Eddy: "The labs can say, 'We took our tip-top, totally unrestricted, most cyber-capable model, and we told it to go absolutely insane, and we spent $20 million, $50 million trying to break this scheme.'"

"We actually have the prospect to have more confidence in our security than ever before."

@eddylazzarin@danboneh

Your browser does not support the video tag.

🔗 View on Twitter

a16z crypto@a16zcrypto

AI is getting better at solving hard math problems, especially given recent drops. But what does that mean for the cryptography that protects everything from private messages to healthcare to banking and crypto wallets?

Stanford cryptographer Dan Boneh, with @eddylazzarin, @SuccinctJT, @liz_harkavy, and @smc90, makes sense of the warnings sounded recently. They cover:

- what’s fact vs. assumption
- what’s new and what risks cryptographers have long prepared for
- where quantum computing doesn't (and does) fit
- opportunities for builders

Highlights:
00:37 – What sparked the most recent debate about AI and cryptography
01:35 – Signal vs. noise: What the math does and doesn’t tell us
05:29 – Why cryptography depends on open problems
09:25 – Why the stakes go beyond blockchains
10:43 – How AI could strengthen confidence in cryptography
13:50 – Considerations for users and institutions
20:10 – Where AI and quantum threats overlap
22:32 – Building systems that can change cryptographic methods
23:59 – Hash-based versus lattice-based cryptography
33:07 – Opportunities for builders, and better security tools
38:14 – What comes next if there were a cryptographic break?

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬21🔄25❤️150👀31168📊32

⚡ Powered by xgo.ing

Travis Kalanick on the founder's paradox: the better you get, the harder it is to stay fired up. "T...

a16z (@a16z) — Tue, 13 Oct 2026 19:05:17 GMT

Travis Kalanick on the founder's paradox: the better you get, the harder it is to stay fired up.

"The young entrepreneurs that are coming up right now don't totally get it."

"When you get into your 3rd and 4th company, I don't know, this may be even my 5th at this point, you get fricking really good at doing stuff."

"Things that used to take me a day or three days, a lot of stress, anxiety, like, 'How am I gonna do this?' take me like 45 minutes. And that's a beautiful thing."

"I'm so used to adversity at this point that it's kind of normal, so I don't get as pissed off. But being pissed off makes you good as an entrepreneur, so I gotta make sure I'm still fired up."

"You get too used to adversity, and that's kind of a problem."

Full conversation: youtube.com/watch?v=r8qKNF…@travisk

Your browser does not support the video tag.

🔗 View on Twitter

💬49🔄74❤️1282👀155748📊234

⚡ Powered by xgo.ing

Patrick Collison with Amjad Masad on why AI will be like food: essential to everything, and still no...

a16z (@a16z) — Tue, 13 Oct 2026 17:10:36 GMT

Patrick Collison with Amjad Masad on why AI will be like food: essential to everything, and still not where most of the value goes.

Amjad: "There's also this trend of saying that the foundation lab companies are gonna be able to capture a big part of at least new business creation, but perhaps the economy. It's like there's no reason to compete with ChatGPT or Claude because they're just gonna roll over us."

Patrick: "Without food, there really would not be much economy or humanity. It is a truly necessary ingredient in production. And yet, most of the economy is not captured by food producers."

"The idea that AI is going to be hugely important and abundant in the future does not necessarily mean that all the value is gonna be captured by these AI labs."

"The value of any company is in proportion to what its customers can pay it. And so if there's one company in the economy, that's in fact not a very valuable company... Prices and value accrue to that which is scarce."

"I think it would be a terrible reason not to start a company because you think that... any given company or sector will gobble everything up. That's not how things have worked through history."

Full conversation: youtube.com/watch?v=YgYiF8…@patrickc@stripe@amasad@Replit

Your browser does not support the video tag.

🔗 View on Twitter

💬26🔄21❤️214👀34524📊54

⚡ Powered by xgo.ing

Gavin Baker on the AI race as the highest-stakes game of corporate chess ever played: "You've got t...

a16z (@a16z) — Tue, 13 Oct 2026 00:12:22 GMT

Gavin Baker on the AI race as the highest-stakes game of corporate chess ever played:

"You've got to give Meta a lot of credit. They were out of the game, and they got back in the game. It's just kind of amazing."

"Who could have imagined a year ago, when Gemini was ascendant, that Gemini wouldn't even be in the conversation, and Muse and Meta would be significantly ahead of them from a capability perspective."

"Some people have made bad moves, they've made good moves. You've seen some people come out of the game, others come back in."

"I don't know if we're going to call it multi-model, a hybrid model. I don't know what terminology the world is going to settle on, but I think that's the future."

@GavinSBaker@DavidGeorge83

Your browser does not support the video tag.

🔗 View on Twitter

💬38🔄41❤️561👀152093📊102

⚡ Powered by xgo.ing

optimism wins

a16z (@a16z) — Mon, 12 Oct 2026 21:30:04 GMT

optimism wins

💬85🔄271❤️4153👀171411📊517

⚡ Powered by xgo.ing

.@flexport CEO Ryan Petersen on the Charlie Munger advice he passes on to AI founders: "The key to s...

a16z (@a16z) — Mon, 12 Oct 2026 20:00:32 GMT

.@flexport CEO Ryan Petersen on the Charlie Munger advice he passes on to AI founders: "The key to success is dumb competition."

"One of my favorite things that Charlie told me at one of these dinners at his house was when I told him about Flexport, he was like, 'Oh, you have a great business because the key to success is dumb competition.'"

"I always say that to people who are building AI companies. What a nightmare. Your competitors are literally AI geniuses."

"You should find a way to apply AI in some other domain so you're not competing with the smartest people on Earth."

"It's like trying to be a boxer. You might beat all the people in your local gym, but at some point you're gonna run into somebody who's gonna kick your ass."

@typesfast w/ @shaneparrish

Your browser does not support the video tag.

🔗 View on Twitter

💬42🔄80❤️1326👀109045📊265

⚡ Powered by xgo.ing

https://t.co/URbd8O8R0Z CEO Dhaval Adjodah and CTO Owen He on building a self-improving AI company w...

a16z (@a16z) — Mon, 12 Oct 2026 17:45:35 GMT

MakerMaker.AI CEO Dhaval Adjodah and CTO Owen He on building a self-improving AI company where humans and agents work as one system:

"One big limit of [RSI] is the human element. I think the way that a lot of AI companies are thinking is, let us use AI to replace humans."

"But what we found is that there's so much that good researchers and good engineers can bring to the table that's gonna be very hard to replace by agents."

"And so that's why from the very beginning, we've tried to think of our basically entire company as a multi-agent system, where humans are a kind of agent that's cohabitating with other AI agents."

"We have one of our engineers that we named the Viceroy of Variance. That's a real thing. His job is to go and destroy the results we have and find what's wrong."

"So far we have not found a replacement for what we call quality time. Literally running hundreds of experiments, looking at it, developing an intuition, trying to figure out what is wrong, what is not wrong... what is just variance, what is not."

@makermaker_ai@_dval_

Your browser does not support the video tag.

🔗 View on Twitter

a16z@a16z

MakerMaker.AI co-founders Dhaval Adjodah and Owen He with a16z's Martin Casado on recursive self-improvement, and how to tell when it's real:

A lot of RSI pitches are really just people using AI to build better AI. Tech has always worked that way. You needed computers to build chips. Steam engines got better once you could get coal faster.

Dhaval and Owen's bar is higher. The model has to improve itself: its weights, its harness, even the prompt that tells it what to work on next.

And the gains have to hold up when you count what they cost. They track everyone's experiment spend on a "bleeder board," and one engineer, the Viceroy of Variance, has the job of breaking their results.

Over the past few months, they've started seeing early signs of their system getting better and cheaper at the same time.

55 Why most RSI pitches miss
25 Optimizer vs. optimizee
10 The RSI equation: performance over cost
50 Why a closed loop does not mean takeoff
40 Humans are agents inside the system too
25 The denominator trap: progress divided by capital raised
40 Training GPT-2 for $100
40 Most RSI papers fail when replicated
40 Why you have to co-optimize
40 Why inference is the first problem
15 The agent rewrites its own prompt
20 "It's not a belief. It's happening."
00 Why they haven't published
50 Intelligence is a vector and Amdahl's law
45 The garden hose theory of RSI

YouTube: youtu.be/3Q1mdn5ZGvM@makermaker_ai@_dval_@martin_casado

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬15🔄5❤️66👀23918📊16

⚡ Powered by xgo.ing

a16z's Martin Casado with https://t.co/URbd8O8R0Z CTO Owen He on why a lot of RSI pitches are sellin...

a16z (@a16z) — Mon, 12 Oct 2026 16:25:24 GMT

a16z's Martin Casado with MakerMaker.AI CTO Owen He on why a lot of RSI pitches are selling something as old as the steam engine:

Martin: "I see a lot of pitches come in, and a lot of them use the term RSI. And I'll be like, 'Oh, so what do you do?' And they'll say, 'We use AI to create AI.' 'That sounds very interesting. So what do you do?' 'We use AI to create GPU kernels.'"

Owen: "RSI is definitely an overloaded term today... The important thing when you are differentiating them is to ask the question, what is the optimizer and what is the optimizee? When you're self-improving, who is improving?"

"There are people who are saying, 'Okay, we're doing RSI. We have humans using AI to develop better AI.' But that to us is just autocatalysis... the ideas and the improvement come from human brains."

Martin: "Most technical epochs have had autocatalytic effects. There's no way you can create a computer chip without a computer... You could build better steam engines because you could get coal faster, etc. So this notion of using the thing to build the thing faster is not new."

Owen: "That human brain was never the optimizee. Now, for the first time, we could swap the optimizer as the optimizee... The brain of the agents, which is the LLM, the model, can be the thing that you're optimizing, and this leads to this reinvestment of cognitive improvement into itself."

@makermaker_ai@martin_casado

Your browser does not support the video tag.

🔗 View on Twitter

a16z@a16z

MakerMaker.AI co-founders Dhaval Adjodah and Owen He with a16z's Martin Casado on recursive self-improvement, and how to tell when it's real:

A lot of RSI pitches are really just people using AI to build better AI. Tech has always worked that way. You needed computers to build chips. Steam engines got better once you could get coal faster.

Dhaval and Owen's bar is higher. The model has to improve itself: its weights, its harness, even the prompt that tells it what to work on next.

And the gains have to hold up when you count what they cost. They track everyone's experiment spend on a "bleeder board," and one engineer, the Viceroy of Variance, has the job of breaking their results.

Over the past few months, they've started seeing early signs of their system getting better and cheaper at the same time.

55 Why most RSI pitches miss
25 Optimizer vs. optimizee
10 The RSI equation: performance over cost
50 Why a closed loop does not mean takeoff
40 Humans are agents inside the system too
25 The denominator trap: progress divided by capital raised
40 Training GPT-2 for $100
40 Most RSI papers fail when replicated
40 Why you have to co-optimize
40 Why inference is the first problem
15 The agent rewrites its own prompt
20 "It's not a belief. It's happening."
00 Why they haven't published
50 Intelligence is a vector and Amdahl's law
45 The garden hose theory of RSI

YouTube: youtu.be/3Q1mdn5ZGvM@makermaker_ai@_dval_@martin_casado

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬5🔄10❤️79👀30580📊14

⚡ Powered by xgo.ing

https://t.co/URbd8O8R0Z co-founders Dhaval Adjodah and Owen He with a16z's Martin Casado on recursiv...

a16z (@a16z) — Mon, 12 Oct 2026 15:25:45 GMT

MakerMaker.AI co-founders Dhaval Adjodah and Owen He with a16z's Martin Casado on recursive self-improvement, and how to tell when it's real:

A lot of RSI pitches are really just people using AI to build better AI. Tech has always worked that way. You needed computers to build chips. Steam engines got better once you could get coal faster.

Dhaval and Owen's bar is higher. The model has to improve itself: its weights, its harness, even the prompt that tells it what to work on next.

And the gains have to hold up when you count what they cost. They track everyone's experiment spend on a "bleeder board," and one engineer, the Viceroy of Variance, has the job of breaking their results.

Over the past few months, they've started seeing early signs of their system getting better and cheaper at the same time.

55 Why most RSI pitches miss
25 Optimizer vs. optimizee
10 The RSI equation: performance over cost
50 Why a closed loop does not mean takeoff
40 Humans are agents inside the system too
25 The denominator trap: progress divided by capital raised
40 Training GPT-2 for $100
40 Most RSI papers fail when replicated
40 Why you have to co-optimize
40 Why inference is the first problem
15 The agent rewrites its own prompt
20 "It's not a belief. It's happening."
00 Why they haven't published
50 Intelligence is a vector and Amdahl's law
45 The garden hose theory of RSI

YouTube: youtu.be/3Q1mdn5ZGvM@makermaker_ai@_dval_@martin_casado

Your browser does not support the video tag.

🔗 View on Twitter

💬14🔄12❤️105👀69293📊27

⚡ Powered by xgo.ing

John Collison on why @Stripe calls January 1st, 2026 "day one of the singularity," and the record hi...

a16z (@a16z) — Sun, 11 Oct 2026 21:30:35 GMT

John Collison on why @Stripe calls January 1st, 2026 "day one of the singularity," and the record high of new companies being created:

"The Unix epoch is January 1st, 1970. We decided that the singularity epoch is January 1st, 2026... We are now in the singularity."

"It's not a total coincidence that we picked that date. It was the end of last year that I feel like for many of us it was the 'holy crap' moment when it came to coding models and how they're getting so good."

"All the AI folks talk about this idea of recursive self-improvement where the AI uses the AI to make itself better... People refer to the AI as this monolithic thing, but we are clearly in a world where AI capabilities are helping with the rate of improvement in AI."

"As a result of intelligence on tap getting much cheaper, new firm creation is way up. We're seeing the highest rates of new firm creation that we've seen in recent memory on Stripe, right now."

"Because when you have all these tools, it's easier to start a company, it's easier to actually make it successful."

"I think this technology has a lot to be optimistic about. And so we don't see the singularity in any kind of a pessimistic or worried manner. We are in this moment of AI takeoff right now."

@collision w/ @mollyoshea

Your browser does not support the video tag.

🔗 View on Twitter

Andrew Curran@AndrewCurran_

Stripe sent a letter to investors this morning saying they believe The Singularity has begun, and that we passed the threshold on January 1st 2026.

🔗 View Quoted Tweet

💬35🔄28❤️199👀43328📊47