a16z(@a16z)

a16z's Alex Immerman, David George, and Sarah Wang on why a data center can lower your electricity b...

a16z (@a16z) — Wed, 07 Oct 2026 22:00:35 GMT

a16z's Alex Immerman, David George, and Sarah Wang on why a data center can lower your electricity bill:

"It may seem counterintuitive, but a data center can help lower your electricity rate. A recent US study showed that for every 10% increase in data center capacity, residential rates went down by 40 bps."

"You should think about it as a power grid is a shared fixed cost base: poles, wires, substations. A large stable customer, like a data center, can help spread those costs across more units of electricity. Simply put, more demand across a shared system is a positive."

"Investment in shared infrastructure can also improve the economics for the households and businesses connected to it."

"There's real examples of that today. It's not just lip service."

Still, 56% of Texas voters say more data centers would raise their power bills.

The catch is it depends on who pays. When developers cover their own grid upgrades instead of pushing them onto everyone else's bills, the savings are real.

a16z's @rmcentush on how to get that right: a16z.news/p/why-texas-is…@aleximm@DavidGeorge83@sarahdingwang

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Ryan McEntush@rmcentush

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The US grid plans to add 86 gigawatts this year. Here's where it's coming from: - Solar: 43.4 GW - ...

a16z (@a16z) — Wed, 07 Oct 2026 20:50:03 GMT

The US grid plans to add 86 gigawatts this year. Here's where it's coming from:

- Solar: 43.4 GW
- Batteries: 24.3 GW
- Wind: 11.8 GW
- Gas: 6.3 GW
- New nuclear: 0 GW

The AI labs are planning for hundreds of gigawatts. Solar is the one source already produced at that scale, and most of it is made in China.

"Scaling firm power that isn't gas is much more complicated. Uprates and restarts can squeeze a little more from the existing nuclear fleet, but the real upside is new reactors."

a16z's @rmcentush on how America powers the next wave of growth: a16z.news/p/why-texas-is…

Ryan McEntush@rmcentush

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.@basepowerco has raised $2.5B to bring affordable, reliable energy to people's homes across America...

a16z (@a16z) — Wed, 07 Oct 2026 19:30:30 GMT

.@basepowerco has raised $2.5B to bring affordable, reliable energy to people's homes across America.

They're building a new nationwide power company by rebuilding every layer from scratch.

Three years in:
- Built a home battery factory in 8 months
- Installing 100 batteries a day
- Powering 30,000+ homes

And they're hiring!

Open roles in Austin, TX:
- Head of Firmware
- Backend Engineer (BaseOS)
- Product Engineer
- Electronics Design Engineer
- System Integration Engineer
- Deployment Finance & BizOps

(Bay Area office opening soon, too.)

More roles and the full interview with Head of Software @JaredMGreene on a16z Jobs: a16zjobs.substack.com/p/base-power-h…

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a16z@a16z

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We're excited to invest in Preference Model. Building RL environments that actually work is harder ...

a16z (@a16z) — Wed, 07 Oct 2026 18:02:49 GMT

We're excited to invest in Preference Model.

Building RL environments that actually work is harder than it looks. Models are relentless at reward hacking, finding shortcuts, and exploiting vulnerabilities.

@preferencemodel has focused on the domain that matters most to labs right now: AI research and ML engineering itself.

Over the past year, the team has built RL environments for leading labs. Their focus has been on building the infrastructure to make harder, more resistant environments as models improve: tooling that finds where models are weak, generates new tasks to target those gaps, and tests environments against agents actively trying to break it.

This week they're open-sourcing Karotte, the framework they've used in production — hardened through more than a million evaluation runs and red-teaming.

We're thrilled to partner with @chem_safety and @Ning_Catsnail and the Preference Model team as they build the training grounds for capable and aligned models.

By @JenniferHli

Jennifer Zhou@chem_safety

Today we are open-sourcing 🥕Karotte, our framework for building RL environments.

We've used it for the past year to build MLE RL environments for frontier labs, and it's been hardened through 1M+ evaluation runs and red-teaming.

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Left: Texas's grid only maxes out about 200 hours a year, roughly 2% of the time. Right: New data c...

a16z (@a16z) — Wed, 07 Oct 2026 17:30:09 GMT

Left: Texas's grid only maxes out about 200 hours a year, roughly 2% of the time.

Right: New data centers that agree to cut back during peak hours can skip the wait for more capacity. US grids could add 100 GW of them without building a single new plant.

a16z's @rmcentush on how flexible data centers get onto the grid sooner: a16z.news/p/why-texas-is…

Ryan McEntush@rmcentush

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Texas just froze new data center permits: - Its queue went from 63 GW to 474 GW in 18 months, more ...

a16z (@a16z) — Wed, 07 Oct 2026 16:40:02 GMT

Texas just froze new data center permits:

- Its queue went from 63 GW to 474 GW in 18 months, more than 5x record peak demand

- Only 9.5 GW is approved and about 4.3 GW is actually running

- The rest is duplicates, speculation, and developers who have never plugged in a GPU

a16z's @rmcentush on why the grid can't build for that many maybes: a16z.news/p/why-texas-is…

Ryan McEntush@rmcentush

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"At the end of 2024, Texas' grid operator had 63 GW of large new customers in its queue. By this Jun...

a16z (@a16z) — Wed, 07 Oct 2026 15:45:09 GMT

"At the end of 2024, Texas' grid operator had 63 GW of large new customers in its queue. By this June, that figure reached 474 GW, more than five times record peak demand."

"Then Texas hit pause."

"Developers routinely spam requests across several sites, and many are speculative builds with no customer yet."

"Planners can't tell which are real, and they don't want ratepayers covering upgrades built for projects that never show up."

"Fully connecting a large data center can take 5 to 10 years... Idle GPUs cost far more than the electricity to run them, so speed matters most... That's why developers are planning to bring their own power on site."

"Full reliability for these power islands is very, very hard, but some labs and hyperscalers have shown that, forced to choose between reliability and speed, they'll pick speed."

"Every site I'm aware of intends to connect to the grid as soon as it can, chiefly because on-site power almost always costs more."

"AI companies have pledged to pay their way, and most would rather connect to the grid than avoid it. It seems ideal for everyone that we let them do so instead of driving that spending into private islands."

"Texas created the energy fast lane first; now it has the opportunity to show us a better one, and I expect it will. Power is where America finds out whether it can still build."

a16z's @rmcentush on why Texas is making data centers wait: a16z.news/p/why-texas-is…

Ryan McEntush@rmcentush

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🇯🇵

a16z (@a16z) — Wed, 07 Oct 2026 15:07:01 GMT

🇯🇵

Raghu Raghuram@RaghuRaghuram

a16z is officially in Japan, led by Hitoshi Yoshida as Head of Japan, with the opening of our Japan office.

It was an honor welcoming Governor Koike at the opening earlier today. Japan combines world-class industrial capabilities with growing innovation in AI, cybersecurity, robotics, and defense. By bringing our portfolio companies and the Japanese ecosystem together, we aim to accelerate Japan’s AI-native economy and strengthen the ties between Silicon Valley and Japan.

As the former President of Microsoft Japan and Hewlett-Packard Japan, Hitoshi brings decades of technology leadership and deep relationships across business, industry, and government. We’re thrilled to have him lead our efforts in Japan and look forward to doing great work in partnership with the Japanese technology community.

日本代表として吉田仁志を迎え、日本オフィスを開設できたことを大変心強く光栄に思います。日本は、世界有数の産業基盤と、AI・サイバーセキュリティ・ロボティクス・防衛などの分野における目覚ましいイノベーションを兼ね備えています。
我々はこれまでになかった機会を、日本の若い世代に提供していくことを目指します。日本のテクノロジーコミュニティの皆様と共に、日本の市場やエコシステムの発展に貢献していけることを楽しみにしています。

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Instagram turns 16 today

a16z (@a16z) — Wed, 07 Oct 2026 00:08:32 GMT

Instagram turns 16 today

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.@Valon just raised a $150M Series D at a $2.3B valuation. Within six months of selling software, th...

a16z (@a16z) — Tue, 06 Oct 2026 22:45:16 GMT

.@Valon just raised a $150M Series D at a $2.3B valuation. Within six months of selling software, they signed over $200M in deals.

(And they're hiring!)

How the seven-year-old company got there:

- Mortgage is $13 trillion of consumer debt running on a system built before the internet, and no servicer will trust a new platform. So Valon became one.

- Co-founder Andrew Wang read every federal and state regulation, 18 hours a day for six months, and turned it into code.

- They ran their own servicer on the software until it hit 3x the industry's efficiency, sold that servicer to a larger mortgage company, and now sell the software to everyone else. One of the biggest servicers in the US is moving 4 million loans onto it, nearly 10% of the market.

Valon is hiring deployment strategists in NY and SF. Read more about their open roles: a16zjobs.substack.com/p/valon-just-r…@xlindadu@wangandrewd

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a16z@a16z

a16z's Angela Strange sits down with @Valon's Andrew Wang and Linda Du to unpack what it takes to rebuild the infrastructure underneath a $13 trillion mortgage market that still relies heavily on systems designed before the internet.

Linda and Andrew explain why Valon chose the hardest path: becoming a regulated mortgage servicer, translating decades of federal and state regulation into software, and proving the platform on its own loans before selling it to the industry. That foundation made Valon roughly 3x as efficient as traditional servicing and created the system of record it is now using to bring AI into complex mortgage workflows.

They also discuss what AI makes possible on top of that infrastructure, from agents handling long-tail servicing tasks to voice interfaces and more personalized customer experiences. And they explain why deploying the technology into large regulated enterprises is ultimately as much a change-management challenge as a technical one.

01:02 From Soros to mortgage math
03:00 $13 trillion still on 1960s systems
09:38 Why build the whole stack from scratch
11:47 The 3-5 year licensing gauntlet
15:50 Turning 50 states of regulation into code
18:00 Winning the first customers
22:34 What AI unlocks now
29:47 Everything is servicing
32:52 $200M in deals within six months

YouTube: youtube.com/watch?v=ZBDGuT…@wangandrewd@xlindadu@astrange

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Traffic and revenue tell two different stories Replit is a top 10 consumer AI app by spend and a bo...

a16z (@a16z) — Tue, 06 Oct 2026 22:00:20 GMT

Traffic and revenue tell two different stories

Replit is a top 10 consumer AI app by spend and a bottom 5 app by traffic

AI power users' money shows up where casual users don't

Full list of the Top 50 Consumer AI Apps by Revenue, from @omooretweets on Cosign: cosign.co/lists/omooretw…

a16z@a16z

The top 1% of AI spenders now outspend the bottom 50% combined

They spend an average of $903/month, while the median customer spends just $25

More charts in our Top 100 Consumer AI Apps breakdown: a16z.news/p/top-100-cons…

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What are the top 1% of AI spenders paying so much money for? Tools to build, automate, and ship. V...

a16z (@a16z) — Tue, 06 Oct 2026 21:25:52 GMT

What are the top 1% of AI spenders paying so much money for?

Tools to build, automate, and ship.

Versus the average spender, they're:
- 23x more likely to pay for n8n
- 18x for fal and Manus
- 15x for Higgsfield

Full list of the Top 50 Consumer AI Apps by Revenue, from @omooretweets on Cosign: cosign.co/lists/omooretw…

a16z@a16z

The top 1% of AI spenders now outspend the bottom 50% combined

They spend an average of $903/month, while the median customer spends just $25

More charts in our Top 100 Consumer AI Apps breakdown: a16z.news/p/top-100-cons…

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Greg Brockman of @OpenAI on the new security loop: point every model release at your own systems, fi...

a16z (@a16z) — Tue, 06 Oct 2026 20:45:28 GMT

Greg Brockman of @OpenAI on the new security loop: point every model release at your own systems, find the vulnerabilities, automate it.

"We took 25% of our production engineers and said, 'Sorry, all your projects are on hold. You are now defending. You are now up-leveling our security architecture. You're going to use the models to find all the holes.' And we found a number of serious issues, and we fixed them."

"We found some new problems, but eventually it saturated. We basically have found, to our knowledge, all of the P0s, all of the critical problems that Astra is smart enough to find. And of course, there will be a new model, there will be a new round."

"You want to be in this tight loop of new cyber capability drops, you deploy it against your systems, you find the new holes, and ideally, you've managed to automate this, what we call defense factory. That's what we're building internally."

@gdb@bhorowitz

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a16z@a16z

Kevin Mandia on why companies need an agent swarm polling their network like a heartbeat, because the window to catch a new security hole keeps shrinking:

"We do a thing called a hyperattack. That's just a fancy word for we throw a drone swarm of agents at you, and we map your network. Every service, every route, every system, all assets."

"With that metadata, we now just poll you almost like a heartbeat. What's changed? Did an app change? Did a route change? Did a service get updated? So that we can poll cheaply for change and then attack the change."

"What you really want in the AI age is the constant pressure of models attacking you... You do it when either the threat changes, new models come out, new intelligence is available, or your network changes."

"That's what we had over the weekend. There was a zero-day in a popular product, and immediately we've already got the heartbeat. We just polled who's got the problem."

"Our goal at Armadin is to go from a known vulnerability to knowing whether it's actually exploitable before the bad guys can."

@ArmadinSecurity@DavidGeorge83

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The window to patch software bugs is collapsing Of the bugs hackers actually exploit, ~87% are now ...

a16z (@a16z) — Tue, 06 Oct 2026 20:15:02 GMT

The window to patch software bugs is collapsing

Of the bugs hackers actually exploit, ~87% are now being attacked on or before the day the bug is public knowledge

That share was 23% in 2020

a16z@a16z

Kevin Mandia on why companies need an agent swarm polling their network like a heartbeat, because the window to catch a new security hole keeps shrinking:

"We do a thing called a hyperattack. That's just a fancy word for we throw a drone swarm of agents at you, and we map your network. Every service, every route, every system, all assets."

"With that metadata, we now just poll you almost like a heartbeat. What's changed? Did an app change? Did a route change? Did a service get updated? So that we can poll cheaply for change and then attack the change."

"What you really want in the AI age is the constant pressure of models attacking you... You do it when either the threat changes, new models come out, new intelligence is available, or your network changes."

"That's what we had over the weekend. There was a zero-day in a popular product, and immediately we've already got the heartbeat. We just polled who's got the problem."

"Our goal at Armadin is to go from a known vulnerability to knowing whether it's actually exploitable before the bad guys can."

@ArmadinSecurity@DavidGeorge83

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Kevin Mandia on why companies need an agent swarm polling their network like a heartbeat, because th...

a16z (@a16z) — Tue, 06 Oct 2026 19:45:20 GMT

Kevin Mandia on why companies need an agent swarm polling their network like a heartbeat, because the window to catch a new security hole keeps shrinking:

"We do a thing called a hyperattack. That's just a fancy word for we throw a drone swarm of agents at you, and we map your network. Every service, every route, every system, all assets."

"With that metadata, we now just poll you almost like a heartbeat. What's changed? Did an app change? Did a route change? Did a service get updated? So that we can poll cheaply for change and then attack the change."

"What you really want in the AI age is the constant pressure of models attacking you... You do it when either the threat changes, new models come out, new intelligence is available, or your network changes."

"That's what we had over the weekend. There was a zero-day in a popular product, and immediately we've already got the heartbeat. We just polled who's got the problem."

"Our goal at Armadin is to go from a known vulnerability to knowing whether it's actually exploitable before the bad guys can."

@ArmadinSecurity@DavidGeorge83

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a16z@a16z

.@ArmadinSecurity is a new company that uses AI agents to attack your network before criminals do, finding real exploitable zero-days and eventually patching them autonomously.

Their agents map every service, route, and system from the outside, then re-attack whenever something changes. Since January that's uncovered 90+ zero-days at Fortune 500 companies.

Kevin Mandia spent 30 years responding to some of the worst breaches in the world and built Mandiant, now a core part of Google Cloud's security arm. He had no plans to start another company until AI arrived and changed everything: in his words, "everything I did is dead." He joins a16z's David George to talk about why he came back and what Armadin is building.

In this conversation, they cover what AI attacks look like today, why nation states hack like a sniper and AI hacks like a drone swarm, and why the only defense fast enough to keep up is one with no humans in the loop.

05 "Everything I did is dead"
10 AI on offense, AI on defense
20 What an AI attack looks like today
20 Sniper rounds vs drone swarms
25 Map everything, attack what changes
35 The problem with pen tests
40 90+ zero-days at Fortune 500s
35 A bad patch beats an intrusion
55 Why humans are too slow for the SOC
30 Why the next year in cyber won't be pretty
30 How Mythos changed the threat
15 Caging the attacker Armadin built
05 Open and closed models tied on offense
40 AI is finding zero-days on its own
45 Mandiant in 2004 vs Armadin in 2026
20 Why Armadin retrains sales every week

YouTube: youtu.be/cJsHel27Z6M@ArmadinSecurity@DavidGeorge83

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a16z's Angela Strange sits down with @Valon's Andrew Wang and Linda Du to unpack what it takes to re...

a16z (@a16z) — Tue, 06 Oct 2026 19:03:14 GMT

a16z's Angela Strange sits down with @Valon's Andrew Wang and Linda Du to unpack what it takes to rebuild the infrastructure underneath a $13 trillion mortgage market that still relies heavily on systems designed before the internet.

Linda and Andrew explain why Valon chose the hardest path: becoming a regulated mortgage servicer, translating decades of federal and state regulation into software, and proving the platform on its own loans before selling it to the industry. That foundation made Valon roughly 3x as efficient as traditional servicing and created the system of record it is now using to bring AI into complex mortgage workflows.

They also discuss what AI makes possible on top of that infrastructure, from agents handling long-tail servicing tasks to voice interfaces and more personalized customer experiences. And they explain why deploying the technology into large regulated enterprises is ultimately as much a change-management challenge as a technical one.

01:02 From Soros to mortgage math
03:00 $13 trillion still on 1960s systems
09:38 Why build the whole stack from scratch
11:47 The 3-5 year licensing gauntlet
15:50 Turning 50 states of regulation into code
18:00 Winning the first customers
22:34 What AI unlocks now
29:47 Everything is servicing
32:52 $200M in deals within six months

YouTube: youtube.com/watch?v=ZBDGuT…@wangandrewd@xlindadu@astrange

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Greg Brockman of @OpenAI on why AI that finds security holes for attackers to exploit is a blessing ...

a16z (@a16z) — Tue, 06 Oct 2026 18:15:14 GMT

Greg Brockman of @OpenAI on why AI that finds security holes for attackers to exploit is a blessing in disguise for defenders:

"In this case, you saw both an AI that was able to hack out of a secure environment and hack into a company's production environment."

"This capability broadly diffused is something that will really empower threat actors in new ways. Defenders need to use this time before that technology's broadly available to secure themselves."

"If you can find vulnerabilities, if you're an attacker, you can use it for no good, but if you're a defender, you can patch. If you're a defender, you control the battleground. You control the setup of your systems."

"Our belief right now is that there's this window... use these frontier capabilities that you will have differential access to... so that as the frontier capabilities get better, you get pulled along too."

@gdb@bhorowitz

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a16z@a16z

Kevin Mandia on finding 90+ security holes at Fortune 500 companies that nobody knew existed:

"When you have an AI-based attack, it'll find logic flaws rather than code flaws in custom applications. It'll exhaust all routes all the time."

"Armadin, since January of this year, we have found over 90 zero-days at customer sites, all in production."

"We've post-trained all our models with real red teamers, real folks that actually can develop exploits."

"When we're scanning networks, we don't have source code to review. We're not finding these zero-days with source code. We're not finding these zero-days because we can log into an app and now we have access, and we can get to other things. We are black box coming from the internet."

"Over 90 zero-days in major software companies, and they're thankful. We're coming from the outside, and then we're calling a CISO, usually within 48 hours, 'Hey, we've got remote code execution in your DMZ.' And usually from there we're getting in, and they agree with us."

"That's not a pen test. That is like a real adversary coming at you."

@ArmadinSecurity@DavidGeorge83

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Cyber is having a moment Across 21 major software companies, including Apple, AWS, Microsoft, and G...

a16z (@a16z) — Tue, 06 Oct 2026 17:30:05 GMT

Cyber is having a moment

Across 21 major software companies, including Apple, AWS, Microsoft, and Google:

- Reported critical vulnerabilities never cleared 100 per month in four years
- Since spring they've jumped to over 600 per month

a16z@a16z

Kevin Mandia on finding 90+ security holes at Fortune 500 companies that nobody knew existed:

"When you have an AI-based attack, it'll find logic flaws rather than code flaws in custom applications. It'll exhaust all routes all the time."

"Armadin, since January of this year, we have found over 90 zero-days at customer sites, all in production."

"We've post-trained all our models with real red teamers, real folks that actually can develop exploits."

"When we're scanning networks, we don't have source code to review. We're not finding these zero-days with source code. We're not finding these zero-days because we can log into an app and now we have access, and we can get to other things. We are black box coming from the internet."

"Over 90 zero-days in major software companies, and they're thankful. We're coming from the outside, and then we're calling a CISO, usually within 48 hours, 'Hey, we've got remote code execution in your DMZ.' And usually from there we're getting in, and they agree with us."

"That's not a pen test. That is like a real adversary coming at you."

@ArmadinSecurity@DavidGeorge83

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Kevin Mandia on finding 90+ security holes at Fortune 500 companies that nobody knew existed: "When...

a16z (@a16z) — Tue, 06 Oct 2026 16:45:09 GMT

Kevin Mandia on finding 90+ security holes at Fortune 500 companies that nobody knew existed:

"When you have an AI-based attack, it'll find logic flaws rather than code flaws in custom applications. It'll exhaust all routes all the time."

"Armadin, since January of this year, we have found over 90 zero-days at customer sites, all in production."

"We've post-trained all our models with real red teamers, real folks that actually can develop exploits."

"When we're scanning networks, we don't have source code to review. We're not finding these zero-days with source code. We're not finding these zero-days because we can log into an app and now we have access, and we can get to other things. We are black box coming from the internet."

"Over 90 zero-days in major software companies, and they're thankful. We're coming from the outside, and then we're calling a CISO, usually within 48 hours, 'Hey, we've got remote code execution in your DMZ.' And usually from there we're getting in, and they agree with us."

"That's not a pen test. That is like a real adversary coming at you."

@ArmadinSecurity@DavidGeorge83

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We're thrilled to lead General Medicine’s $120M Series B as they build the general store for healthc...

a16z (@a16z) — Tue, 06 Oct 2026 15:46:41 GMT

We're thrilled to lead General Medicine’s $120M Series B as they build the general store for healthcare.

@GeneralMedicine is built on the belief that facilitating access to care is a core part of delivering it. The company brings patient history, ongoing clinical guidance, transparent pricing, and help accessing care into one experience.

Its own medical group combined with a marketplace of external clinicians connects patients almost immediately with services far beyond what a single practice could ever provide.

Alongside its direct consumer offering, General Medicine is also building for the enterprise. The same underlying infrastructure for clinical guidance, transparent pricing, and rapid access that General Medicine has already built can be ‘turned on’ for consumers in partnership with any number of enterprise organizations.

We’re excited that the PillPack team is back together. Congrats, @tjparker, @elliotcohen, @ashwinkmurali, and the General Medicine team!

By @vintweeta, @JorgeCondeBio, @daisydwolf, and @evajsteinman

TJ Parker⚡️@tjparker

General Medicine has raised a $120M Series B led by @a16z. We're using it to build the healthcare store.

The only way healthcare will work is to make the customer the most powerful participant in the system. And the only way to do that is to let them shop.

Here’s why healthcare needs a store, and what we had to build to make it possible:

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.@ArmadinSecurity is a new company that uses AI agents to attack your network before criminals do, f...

a16z (@a16z) — Tue, 06 Oct 2026 14:35:03 GMT

.@ArmadinSecurity is a new company that uses AI agents to attack your network before criminals do, finding real exploitable zero-days and eventually patching them autonomously.

Their agents map every service, route, and system from the outside, then re-attack whenever something changes. Since January that's uncovered 90+ zero-days at Fortune 500 companies.

Kevin Mandia spent 30 years responding to some of the worst breaches in the world and built Mandiant, now a core part of Google Cloud's security arm. He had no plans to start another company until AI arrived and changed everything: in his words, "everything I did is dead." He joins a16z's David George to talk about why he came back and what Armadin is building.

In this conversation, they cover what AI attacks look like today, why nation states hack like a sniper and AI hacks like a drone swarm, and why the only defense fast enough to keep up is one with no humans in the loop.

05 "Everything I did is dead"
10 AI on offense, AI on defense
20 What an AI attack looks like today
20 Sniper rounds vs drone swarms
25 Map everything, attack what changes
35 The problem with pen tests
40 90+ zero-days at Fortune 500s
35 A bad patch beats an intrusion
55 Why humans are too slow for the SOC
30 Why the next year in cyber won't be pretty
30 How Mythos changed the threat
15 Caging the attacker Armadin built
05 Open and closed models tied on offense
40 AI is finding zero-days on its own
45 Mandiant in 2004 vs Armadin in 2026
20 Why Armadin retrains sales every week

YouTube: youtu.be/cJsHel27Z6M@ArmadinSecurity@DavidGeorge83

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"The top 1% user is spending $903 per month on AI... The median is spending $25." a16z's Olivia Moo...

a16z (@a16z) — Mon, 05 Oct 2026 22:00:32 GMT

"The top 1% user is spending $903 per month on AI... The median is spending $25."

a16z's Olivia Moore breaks down who actually pays for AI, under 5% of US consumers, with the top 10% of them driving half the revenue:

"About half of Americans report using AI, and about 25% think they're interacting with AI on close to a daily basis."

"The spending data is much more concentrated."

"Our Yipit panel shows around 4.5% of US consumers are paying a subscription to an AI product. Some other sources say as low as 2% or 2.5%. It is expanding, that's doubled from a year ago."

"Even within that top 4.5% who do spend, the top 10% of those are more than half of the revenue, and the top 1% are 20% of the revenue... The top 10% of users are driving this market."

"We could also see what products they're actually spending on. Probably unsurprisingly, it was mostly developer tools, productivity tools, creative tools, things used to build, make, sell."

@omooretweets

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Which tasks do people actually want AI agents to do? More charts in our Top 100 Consumer AI Apps br...

a16z (@a16z) — Mon, 05 Oct 2026 21:00:33 GMT

Which tasks do people actually want AI agents to do?

More charts in our Top 100 Consumer AI Apps breakdown: a16z.news/p/top-100-cons…

a16z@a16z

The seventh edition of our Top 100 Consumer AI Apps is here.

New this time: a revenue leaderboard, alongside the usual web and mobile traffic rankings.

Three years ago we published the first edition. ChatGPT was #1, Claude was unranked, and the entire category was chatbots, image generators, and not much else.

In today's edition:

- ChatGPT still holds the throne, now with 1B+ monthly actives on mobile

- Claude has climbed to #3 on web with nearly 1B monthly visits

- The category has expanded to vibe coding (Lovable, Cursor, Replit), music (Suno), design (Figma), voice (ElevenLabs), video (Higgsfield, Kling), agents (Manus), and even hardware (Plaud)

Full breakdown from @omooretweets: a16z.news/p/top-100-cons…

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"Most people aren't looking to save time, they're looking for ways to spend their time." 9 of 15 co...

a16z (@a16z) — Mon, 05 Oct 2026 20:00:10 GMT

"Most people aren't looking to save time, they're looking for ways to spend their time."

9 of 15 consumer internet categories have zero AI products in the Top 100. These built some of the biggest companies of the last two eras:

- Streaming
- Social
- Dating
- Gaming
- Travel
- Retail
- Finance
- Real estate
- Jobs

More charts in our Top 100 Consumer AI Apps breakdown: a16z.news/p/top-100-cons…

a16z@a16z

The seventh edition of our Top 100 Consumer AI Apps is here.

New this time: a revenue leaderboard, alongside the usual web and mobile traffic rankings.

Three years ago we published the first edition. ChatGPT was #1, Claude was unranked, and the entire category was chatbots, image generators, and not much else.

In today's edition:

- ChatGPT still holds the throne, now with 1B+ monthly actives on mobile

- Claude has climbed to #3 on web with nearly 1B monthly visits

- The category has expanded to vibe coding (Lovable, Cursor, Replit), music (Suno), design (Figma), voice (ElevenLabs), video (Higgsfield, Kling), agents (Manus), and even hardware (Plaud)

Full breakdown from @omooretweets: a16z.news/p/top-100-cons…

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a16z's Olivia Moore thinks consumer AI has its business model backwards: "Substantially all of the ...

a16z (@a16z) — Mon, 05 Oct 2026 19:23:12 GMT

a16z's Olivia Moore thinks consumer AI has its business model backwards:

"Substantially all of the consumer AI revenue thus far has come from direct subscriptions. On our web list, 85% monetize via subscriptions, another 62% via credits or extra usage payments."

"Only 13% had ads or other options where you are the product instead of paying for the product."

"If you look back at the history of the consumer internet, that's a pretty unnatural inversion. Almost all of the really big consumer technology companies we have now make the vast majority of their revenue from ads or transaction fees, not subscriptions."

"The truth is... most people don't have the funds, or don't want to spend their funds, on software."

"If you look at the top 20 consumer subscription products globally, ChatGPT is already on there, which is crazy in three and a half years. Almost all the others are media companies or big platforms like Amazon or Uber, where you have very frequent purchase behavior."

"So I'm a big believer: we have transcended the need for everyone to buy a subscription to AI, and we need to see these other business models come back."

@omooretweets

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a16z@a16z

The seventh edition of our Top 100 Consumer AI Apps is here, including a new dimension this time: what consumers are actually paying for.

a16z's Elena Burger sits down with Olivia Moore and Josh Elman to unpack the current state of consumer AI:

The data reveals a striking power-user economy. Only a small share of consumers currently pay for AI, but among those who do, spending is heavily concentrated at the top. Olivia and Josh discuss why developers, creators, and other power users dominate spending today, and why subscriptions may not be the business model that ultimately brings consumer AI to everyone.

They also dig into the rise of personal agents, the different trajectories of ChatGPT, Claude, and Gemini, how ads could reshape AI economics, and the enormous amount of consumer white space still left to build, from shopping and entertainment to social, dating, and marketplaces.

00:56 Inside the 7th edition
05:55 Muse and Instinct's launch numbers
09:14 The privacy wall for personal agents
14:39 The AI spending power law
20:20 Why subscription is the wrong model
23:51 OpenAI's $1B ad run rate
29:21 ChatGPT vs Claude vs Gemini
32:30 Where creative tools still win
45:12 The white space: dating, shopping, entertainment

YouTube: youtu.be/aCvrzhwUxg0@omooretweets@JoshElman@VirtualElena

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a16z (@a16z) — Mon, 05 Oct 2026 18:30:04 GMT

a16z@a16z

The top 1% of AI spenders now outspend the bottom 50% combined

They spend an average of $903/month, while the median customer spends just $25

More charts in our Top 100 Consumer AI Apps breakdown: a16z.news/p/top-100-cons…

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13 of the 20 biggest consumer subscription bases are media ChatGPT is the first AI app to crack the...

a16z (@a16z) — Mon, 05 Oct 2026 17:40:02 GMT

13 of the 20 biggest consumer subscription bases are media

ChatGPT is the first AI app to crack the top 20

More charts in our Top 100 Consumer AI Apps breakdown: a16z.news/p/top-100-cons…

a16z@a16z

The seventh edition of our Top 100 Consumer AI Apps is here.

New this time: a revenue leaderboard, alongside the usual web and mobile traffic rankings.

Three years ago we published the first edition. ChatGPT was #1, Claude was unranked, and the entire category was chatbots, image generators, and not much else.

In today's edition:

- ChatGPT still holds the throne, now with 1B+ monthly actives on mobile

- Claude has climbed to #3 on web with nearly 1B monthly visits

- The category has expanded to vibe coding (Lovable, Cursor, Replit), music (Suno), design (Figma), voice (ElevenLabs), video (Higgsfield, Kling), agents (Manus), and even hardware (Plaud)

Full breakdown from @omooretweets: a16z.news/p/top-100-cons…

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a16z's Olivia Moore on the untapped category in consumer AI: most people aren't looking to save time...

a16z (@a16z) — Mon, 05 Oct 2026 16:45:10 GMT

a16z's Olivia Moore on the untapped category in consumer AI: most people aren't looking to save time, they're looking for ways to spend it.

"We talked about this in the report, in terms of areas where we have not yet seen AI-native startups pop up."

"@ekuyda, one of our portfolio CEOs, runs a company called Wabi. She has this great quote, I'm probably going to botch it, but it's something like: 'Most people aren't looking to save time, they're looking for ways to spend their time.'"

"This is why social media, entertainment, Netflix, TikTok, YouTube are the most used consumer products we have."

"Much of what we've seen in AI so far is: how do I do this thing a little bit faster? A little bit easier? A little bit more impressively? And that's not an incredibly compelling daily or hourly active value proposition for most people."

@omooretweets

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a16z@a16z

The seventh edition of our Top 100 Consumer AI Apps is here, including a new dimension this time: what consumers are actually paying for.

a16z's Elena Burger sits down with Olivia Moore and Josh Elman to unpack the current state of consumer AI:

The data reveals a striking power-user economy. Only a small share of consumers currently pay for AI, but among those who do, spending is heavily concentrated at the top. Olivia and Josh discuss why developers, creators, and other power users dominate spending today, and why subscriptions may not be the business model that ultimately brings consumer AI to everyone.

They also dig into the rise of personal agents, the different trajectories of ChatGPT, Claude, and Gemini, how ads could reshape AI economics, and the enormous amount of consumer white space still left to build, from shopping and entertainment to social, dating, and marketplaces.

00:56 Inside the 7th edition
05:55 Muse and Instinct's launch numbers
09:14 The privacy wall for personal agents
14:39 The AI spending power law
20:20 Why subscription is the wrong model
23:51 OpenAI's $1B ad run rate
29:21 ChatGPT vs Claude vs Gemini
32:30 Where creative tools still win
45:12 The white space: dating, shopping, entertainment

YouTube: youtu.be/aCvrzhwUxg0@omooretweets@JoshElman@VirtualElena

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The top 1% of AI spenders now outspend the bottom 50% combined They spend an average of $903/month,...

a16z (@a16z) — Mon, 05 Oct 2026 15:45:02 GMT

The top 1% of AI spenders now outspend the bottom 50% combined

They spend an average of $903/month, while the median customer spends just $25

More charts in our Top 100 Consumer AI Apps breakdown: a16z.news/p/top-100-cons…

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The seventh edition of our Top 100 Consumer AI Apps is here, including a new dimension this time: wh...

a16z (@a16z) — Mon, 05 Oct 2026 14:46:53 GMT

The seventh edition of our Top 100 Consumer AI Apps is here, including a new dimension this time: what consumers are actually paying for.

a16z's Elena Burger sits down with Olivia Moore and Josh Elman to unpack the current state of consumer AI:

The data reveals a striking power-user economy. Only a small share of consumers currently pay for AI, but among those who do, spending is heavily concentrated at the top. Olivia and Josh discuss why developers, creators, and other power users dominate spending today, and why subscriptions may not be the business model that ultimately brings consumer AI to everyone.

They also dig into the rise of personal agents, the different trajectories of ChatGPT, Claude, and Gemini, how ads could reshape AI economics, and the enormous amount of consumer white space still left to build, from shopping and entertainment to social, dating, and marketplaces.

00:56 Inside the 7th edition
05:55 Muse and Instinct's launch numbers
09:14 The privacy wall for personal agents
14:39 The AI spending power law
20:20 Why subscription is the wrong model
23:51 OpenAI's $1B ad run rate
29:21 ChatGPT vs Claude vs Gemini
32:30 Where creative tools still win
45:12 The white space: dating, shopping, entertainment

YouTube: youtu.be/aCvrzhwUxg0@omooretweets@JoshElman@VirtualElena

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The seventh edition of our Top 100 Consumer AI Apps is here. New this time: a revenue leaderboard, ...

a16z (@a16z) — Mon, 05 Oct 2026 14:02:42 GMT

The seventh edition of our Top 100 Consumer AI Apps is here.

New this time: a revenue leaderboard, alongside the usual web and mobile traffic rankings.

Three years ago we published the first edition. ChatGPT was #1, Claude was unranked, and the entire category was chatbots, image generators, and not much else.

In today's edition:

- ChatGPT still holds the throne, now with 1B+ monthly actives on mobile

- Claude has climbed to #3 on web with nearly 1B monthly visits

- The category has expanded to vibe coding (Lovable, Cursor, Replit), music (Suno), design (Figma), voice (ElevenLabs), video (Higgsfield, Kling), agents (Manus), and even hardware (Plaud)

Full breakdown from @omooretweets: a16z.news/p/top-100-cons…

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Ben Horowitz on the rap group he started at 18, and why hip-hop became the source material for how h...

a16z (@a16z) — Sun, 04 Oct 2026 20:11:48 GMT

Ben Horowitz on the rap group he started at 18, and why hip-hop became the source material for how he thinks about business:

"I was probably 12 when Rapper's Delight came out... The first one where I was really like, 'Oh, this is gonna be very different,' was when Run-DMC's Hard Times came out. This has a whole other feel to it than anything I've heard."

"The thing that made me locked into hip-hop: I went to school in New York. I have this friend who I grew up with who had gotten shot in the face in some kind of gun accident, and he was blind... I'd call home to my mom: 'Is he gonna be okay?' She's like, 'He hasn't spoken to anybody since he's been shot.'"

"Being a dumb 18-year-old kid, I was like, 'Hip-hop is so great. You can have nothing and you'll rap about your shoes. He needs that feeling.' So I would record the DJ Red Alert and Chuck Chill Out show. Everybody else would be out Saturday night, and I'd be recording. And I would send him the tapes."

"Then I called home to see how he's doing, and they're like, 'Well, he's talking now, but all he'll talk about is hip-hop.'"

"When I went out that summer, we started a hip-hop group called The Blind and Def Crew. Blind and D-E-F. We rapped about being blind. I wrote most of the lyrics: 'The Blind Def Crew, you know we're fly. Three of us, but we got four eyes.'"

"Hip-hop is very business oriented, particularly in the early days. When I started writing on business, so many of the ideas came from those old hip-hop songs. So I started putting the lyrics at the top of the post to give credit where credit was due."

@bhorowitz w/ @MotownIni

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Ben Horowitz on the midnight phone call from Kanye that turned his CEO barbecue into a studio sessio...

a16z (@a16z) — Sun, 04 Oct 2026 18:31:18 GMT

Ben Horowitz on the midnight phone call from Kanye that turned his CEO barbecue into a studio session:

"I answer and go, 'Ye, what's up?' He goes, 'Hey Ben, I heard you're having a barbecue tomorrow. Can I come?' I was like, 'Sure.' 'Okay, great. And I want to record in your studio.'"

"I'm like, 'Ye, I don't have a studio.' And he goes, 'Okay.'"

"6:30 in the morning I get a call. 'Hey Ben, I'm Kanye's engineer. I'm just calling about the specs on your studio.' I'm like, 'I don't have a studio.' He's like, 'What do you mean you don't have a studio? Kanye is expecting a studio.'"

"I'm like, 'Who do we know who has a studio?' And they're like, 'Howie.' So we call. 'What are you doing, Howie?' 'Watching Netflix.' 'Is that studio you have portable?' 'Yeah.' Long story short, you drove the studio down to my house and we set it up in the room."

"A lot more crazy stuff happened. Ye was playing us the Pablo album... That was a legendary day. No doubt."

@bhorowitz w/ @MotownIni

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Replit CEO Amjad Masad on how general models could train smaller, domain-specific models on the fly:...

a16z (@a16z) — Sat, 03 Oct 2026 21:16:24 GMT

Replit CEO Amjad Masad on how general models could train smaller, domain-specific models on the fly:

"There's a lot of talk of recursive self-improvement, but there's something I don't think is getting a lot of discussion, which is models training their replacements."

"You can think of it as a just-in-time compiler. As you're executing dynamic code, the interpreter realizes there's an opportunity to optimize, and it emits machine code on the fly that's a lot more optimized."

"You can imagine general models, you're doing something with Operator or Astra, some of the big models, and they realize the use case is limited, or some other agent observing realizes the use case is limited."

"General agents have all these flaws, but there's also more potential for them to be harmful, more potential for them to go off the rails."

"So the model, on the fly, trains a model that could be its replacement, but is a lot more domain specific. Therefore it's cheaper, less vulnerable to prompt injections, and less harmful for you, because it's less capable."

"It's almost like a system that's training machine learning models for specific use cases as it's monitoring the entire system."

@amasad

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.@OpenRouter co-founder Alex Atallah thinks decision models like Jev could be the alignment layer fo...

a16z (@a16z) — Sat, 03 Oct 2026 20:45:25 GMT

.@OpenRouter co-founder Alex Atallah thinks decision models like Jev could be the alignment layer for agents:

"One of the cool potential applications of Jev and other decision models like it is going to be alignment: checking to see if a tool call or an agent-to-agent communication is aligned."

"There's just so many tool calls. You really need a cheap, fast model if you're going to block something like that... We have a little prototype running internally at OpenRouter."

"Imagine looking at the system prompt and the current tool call, and asking: is this aligned with the original agent's system prompt, and with extra guidelines we didn't tell the agent about?"

"Say you have agents instructed to red team a new product. They should not access the internet, and if they ever do, they should stop right away. But you might not want to explain that to them. You want them to act like bad actors."

"So having another model check every single tool call, to see if it's aligned with something that wasn't in the system prompt, makes sense. And then structural safeguards too... I think companies are probably going to explore a combination of those."

@alexatallah@amasad

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a16z@a16z

.@OpenRouter co-founder Alex Atallah, in his first podcast since Stripe acquired the company, joins @Replit co-founder Amjad Masad and a16z's Erik Torenberg on why the future of AI is independence and specialization.

In this conversation, Alex walks through how the Stripe deal unfolded, why he wasn't originally looking to sell, and why "payments and inference are going to blend together."

Pre-OpenRouter, the typical AI workflow had one model provider to choose from, and little pressure on that provider to lower prices. Now enterprises are diversifying across labs and open-weight models, and every board is asking about AI costs and benchmarks.

Amjad argues if your company depends on one AI lab, it can turn into your competitor. So Replit is building the layer that lets enterprises use any model and any cloud, without being locked into either.

Alex and Amjad are split on personal agents – Amjad runs one agent across his whole company and loves the cross-domain joins, while Alex says general agents cause you to sacrifice understanding, and argues 10 specialized chiefs of staff beats one superagent.

45 How the Stripe deal unfolded
05 Why mixing models beats one model
25 Forcing the labs to compete on price
50 Enterprises want open-weight models
30 Every board asks about AI every month
25 Why companies must own their intelligence
15 Replit as the independence layer
10 Everyone is building the same agent
35 Why Amjad built bring-your-own-cloud
10 Amjad's agent that runs his whole company
55 Why 10 specialized agents beat one
35 Machines, not humans, should specialize
30 Guardrails for agents talking to agents
10 Models training their own replacements
45 Most tasks don't need a frontier model
50 Training small models on Qwen 8B
25 The Rust cycle is coming for AI
10 Fusion models: frontier quality at half the cost

YouTube: youtu.be/ekK8urKHPMQ@alexatallah@OpenRouter@amasad@eriktorenberg

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.@OpenRouter co-founder Alex Atallah on personal agents: 10 specialized chiefs of staff beat one uni...

a16z (@a16z) — Sat, 03 Oct 2026 19:30:26 GMT

.@OpenRouter co-founder Alex Atallah on personal agents: 10 specialized chiefs of staff beat one universal agent. One job each means you can see exactly where it's failing.

"The worst part about doing cross-domain joins with your personal agent is that the more work you give it, the more understanding of what's going on you're sacrificing."

"And no one is taking responsibility for that sacrifice. Agents don't have any responsibility."

"With a universal agent that's doing all things, I can't adjust how much understanding I'm sacrificing."

"It points me towards: maybe down the road, the sub-agents people use will be very vertically focused, and we have a chief of staff type agent that coordinates between them."

"You need vertically focused agents where this agent is more responsible, psychologically, for these things. I want quality checks that make sure it's doing those things correctly. It doesn't need to focus on anything else, it has one focus area."

"I wonder if that's going to help people get at least a loose sense of responsibility on top of agents."

@alexatallah

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.@OpenRouter co-founder Alex Atallah on the "everyone is building the same thing" take: "This remin...

a16z (@a16z) — Sat, 03 Oct 2026 18:00:34 GMT

.@OpenRouter co-founder Alex Atallah on the "everyone is building the same thing" take:

"This reminds me of this tweet I saw... Everybody's building an agent loop with notifications, third-party connectors, context management, memory, sandboxes, agentic web search, an always-on agent on top."

"This product is showing up everywhere. It is showing up everywhere, but these are just the new table-stakes primitives."

"A 2005 version of that tweet would be: 'Oh, everybody's building the same thing. A database, a users table, a sign-in page, a sign-up page, a profile page, a logout page. Everything's the same.'"

"There's like a lot of differentiation really. There's table-stakes needs for AI just like there's table-stakes needs for the web."

@alexatallah@amasad

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a16z@a16z

.@OpenRouter co-founder Alex Atallah, in his first podcast since Stripe acquired the company, joins @Replit co-founder Amjad Masad and a16z's Erik Torenberg on why the future of AI is independence and specialization.

In this conversation, Alex walks through how the Stripe deal unfolded, why he wasn't originally looking to sell, and why "payments and inference are going to blend together."

Pre-OpenRouter, the typical AI workflow had one model provider to choose from, and little pressure on that provider to lower prices. Now enterprises are diversifying across labs and open-weight models, and every board is asking about AI costs and benchmarks.

Amjad argues if your company depends on one AI lab, it can turn into your competitor. So Replit is building the layer that lets enterprises use any model and any cloud, without being locked into either.

Alex and Amjad are split on personal agents – Amjad runs one agent across his whole company and loves the cross-domain joins, while Alex says general agents cause you to sacrifice understanding, and argues 10 specialized chiefs of staff beats one superagent.

45 How the Stripe deal unfolded
05 Why mixing models beats one model
25 Forcing the labs to compete on price
50 Enterprises want open-weight models
30 Every board asks about AI every month
25 Why companies must own their intelligence
15 Replit as the independence layer
10 Everyone is building the same agent
35 Why Amjad built bring-your-own-cloud
10 Amjad's agent that runs his whole company
55 Why 10 specialized agents beat one
35 Machines, not humans, should specialize
30 Guardrails for agents talking to agents
10 Models training their own replacements
45 Most tasks don't need a frontier model
50 Training small models on Qwen 8B
25 The Rust cycle is coming for AI
10 Fusion models: frontier quality at half the cost

YouTube: youtu.be/ekK8urKHPMQ@alexatallah@OpenRouter@amasad@eriktorenberg

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.@OpenRouter co-founder Alex Atallah on what convinced him to do the Stripe deal: full autonomy, fas...

a16z (@a16z) — Sat, 03 Oct 2026 16:45:30 GMT

.@OpenRouter co-founder Alex Atallah on what convinced him to do the Stripe deal: full autonomy, faster execution, same mission.

"We were not thinking about [selling] at all. I did really respect the company, and do really respect it today. Of possible acquisition options for us, it was my top choice."

"As we fleshed out the reasons why it would make sense for both companies, it got more and more interesting."

"It was really clear how aligned they were with us having autonomy over the brand, roadmap, and product, and keeping OpenRouter doing what it's already doing, just much faster, with a much more serious go-to-market plan."

"There was a bigger picture alignment too. Both Stripe and OpenRouter really want lots of new companies in the world. We don't want everyone to be part of one giant company."

"We want to create really good incentives and streamlined workflows for people to start new companies, on top of reliable and price efficient infrastructure and a marketplace that works."

"I really want that future, and Stripe demonstrated they've been wanting it for many, many years. In many ways, payments and inference are going to blend together for companies of the future."

@alexatallah@amasad

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a16z@a16z

.@OpenRouter co-founder Alex Atallah, in his first podcast since Stripe acquired the company, joins @Replit co-founder Amjad Masad and a16z's Erik Torenberg on why the future of AI is independence and specialization.

In this conversation, Alex walks through how the Stripe deal unfolded, why he wasn't originally looking to sell, and why "payments and inference are going to blend together."

Pre-OpenRouter, the typical AI workflow had one model provider to choose from, and little pressure on that provider to lower prices. Now enterprises are diversifying across labs and open-weight models, and every board is asking about AI costs and benchmarks.

Amjad argues if your company depends on one AI lab, it can turn into your competitor. So Replit is building the layer that lets enterprises use any model and any cloud, without being locked into either.

Alex and Amjad are split on personal agents – Amjad runs one agent across his whole company and loves the cross-domain joins, while Alex says general agents cause you to sacrifice understanding, and argues 10 specialized chiefs of staff beats one superagent.

45 How the Stripe deal unfolded
05 Why mixing models beats one model
25 Forcing the labs to compete on price
50 Enterprises want open-weight models
30 Every board asks about AI every month
25 Why companies must own their intelligence
15 Replit as the independence layer
10 Everyone is building the same agent
35 Why Amjad built bring-your-own-cloud
10 Amjad's agent that runs his whole company
55 Why 10 specialized agents beat one
35 Machines, not humans, should specialize
30 Guardrails for agents talking to agents
10 Models training their own replacements
45 Most tasks don't need a frontier model
50 Training small models on Qwen 8B
25 The Rust cycle is coming for AI
10 Fusion models: frontier quality at half the cost

YouTube: youtu.be/ekK8urKHPMQ@alexatallah@OpenRouter@amasad@eriktorenberg

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.@OpenRouter co-founder Alex Atallah, in his first podcast since Stripe acquired the company, joins ...

a16z (@a16z) — Sat, 03 Oct 2026 15:38:36 GMT

.@OpenRouter co-founder Alex Atallah, in his first podcast since Stripe acquired the company, joins @Replit co-founder Amjad Masad and a16z's Erik Torenberg on why the future of AI is independence and specialization.

In this conversation, Alex walks through how the Stripe deal unfolded, why he wasn't originally looking to sell, and why "payments and inference are going to blend together."

Pre-OpenRouter, the typical AI workflow had one model provider to choose from, and little pressure on that provider to lower prices. Now enterprises are diversifying across labs and open-weight models, and every board is asking about AI costs and benchmarks.

Amjad argues if your company depends on one AI lab, it can turn into your competitor. So Replit is building the layer that lets enterprises use any model and any cloud, without being locked into either.

Alex and Amjad are split on personal agents – Amjad runs one agent across his whole company and loves the cross-domain joins, while Alex says general agents cause you to sacrifice understanding, and argues 10 specialized chiefs of staff beats one superagent.

45 How the Stripe deal unfolded
05 Why mixing models beats one model
25 Forcing the labs to compete on price
50 Enterprises want open-weight models
30 Every board asks about AI every month
25 Why companies must own their intelligence
15 Replit as the independence layer
10 Everyone is building the same agent
35 Why Amjad built bring-your-own-cloud
10 Amjad's agent that runs his whole company
55 Why 10 specialized agents beat one
35 Machines, not humans, should specialize
30 Guardrails for agents talking to agents
10 Models training their own replacements
45 Most tasks don't need a frontier model
50 Training small models on Qwen 8B
25 The Rust cycle is coming for AI
10 Fusion models: frontier quality at half the cost

YouTube: youtu.be/ekK8urKHPMQ@alexatallah@OpenRouter@amasad@eriktorenberg

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America has 16,876 machine shops. 83% employ fewer than 20 people. Just nine employ more than 500. ...

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

America has 16,876 machine shops.

83% employ fewer than 20 people. Just nine employ more than 500.

New defense companies are moving from prototypes to mass production, and every missile, drone, ship, and rocket they ultimately produce needs to run through one of these small shops.

That friction is a national security liability.

Connor Love and Collen Larson on the next generation of American manufacturing that gets built by smoothing it: a16z.news/p/the-case-for…

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Lio co-founder and CEO Vlad Keil on the KPI for the company's forward-deployed engineers: automate y...

a16z (@a16z) — Fri, 02 Oct 2026 19:45:22 GMT

Lio co-founder and CEO Vlad Keil on the KPI for the company's forward-deployed engineers: automate your own job, then move to the next one.

"When you look at the org structure of Lio, 85% of the people are engineers."

"And even the people who don't have an engineering title mostly have an engineering background. The reason for that is we don't want to be a consulting company."

"There is a lot of forward-deployed work to do if you go to enterprises because they have different nuances in their processes. But how we work is... building a product in a way where we're reducing this customization, but also where it's a lot of self-service."

"The job of our FDEs is on the one hand making it self-service, but internally it's automating their own job. Their KPI is... literally your job is to automate yourself. And then if you automate yourself, you go to the next task."

@askvladi

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a16z@a16z

Lio co-founder and CEO Vladimir Keil joins a16z's Seema Amble and Elena Burger to discuss where AI-native startups have an advantage when incumbent software companies already own the customer, the data, and the system of record:

Their answer comes down to the work that happens outside those systems. In procurement, a final price in an ERP can hide hundreds of emails, spreadsheets, supplier conversations, engineering analyses, and decisions across legal, finance, and operations. Vlad explains how Lio uses multi-agent systems to take on more of that end-to-end work, from sourcing and RFQs to negotiation, shipment tracking, and invoices.

They also discuss how enterprises learn to trust agents with increasingly consequential decisions, why the last 20% of an internal AI build can require most of the effort, and what happens when both buyers and suppliers have agents working on their behalf.

00:58 Why AI startups still beat incumbents
04:19 The hidden work behind an $8K line item
06:18 Retrieval, process, policy, principal
09:27 The incumbent's internal conflict
14:48 What procurement actually looks like
21:13 Procurement at Boeing-scale
28:03 A bolt order, end-to-end
37:57 What a durable vertical AI company looks like
44:46 When both sides deploy agents

YouTube: youtu.be/OTQ-lFsq7zA@askvladi@seema_amble@VirtualElena

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It takes thousands of suppliers to build one missile, one aircraft, one ship, or one drone. At th...

a16z (@a16z) — Fri, 02 Oct 2026 19:00:22 GMT

It takes *thousands* of suppliers to build one missile, one aircraft, one ship, or one drone.

At the top of the chain is the company that delivers the finished technology.

Beneath it, makers of engines and guidance sensors, actuators and battery packs, circuit cards, and the machine shops that mill, coat, and heat-treat every part.

Silicon Valley funded the top. @ConnorLoveCA and Collen Larson on the case for the rest: a16z.news/p/the-case-for…

Connor Love@ConnorLoveCA

x.com/i/article/2106…

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This is what happens to a rocket engine when the people who design it and the people who build it si...

a16z (@a16z) — Fri, 02 Oct 2026 18:15:03 GMT

This is what happens to a rocket engine when the people who design it and the people who build it sit in the same room.

SpaceX builds roughly 80% of Starship in-house because when you're solving a once-in-a-generation problem, design, manufacturing, and testing have to live in one loop.

Most companies are not SpaceX. They don’t have suppliers in the room with them. And every new defense company can't rebuild American industry inside its own walls.

@ConnorLoveCA and Collen Larson on why the suppliers, and the capital behind them, are the next defense-tech opportunity: a16z.news/p/the-case-for…

Connor Love@ConnorLoveCA

x.com/i/article/2106…

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"When we first backed Sigil Wen, we knew we were backing an unusually resourceful builder. He seeks ...

a16z (@a16z) — Fri, 02 Oct 2026 17:40:16 GMT

"When we first backed Sigil Wen, we knew we were backing an unusually resourceful builder. He seeks out exceptional people, finds ways to help them, earns their trust, and keeps choosing harder problems."

"Sigil came to San Francisco at 17 without much of a network. For a time, he slept at a WeWork. He skipped college to work on AI and spent nearly a year in a hacker house, building alongside researchers and engineers who helped create DALL·E, Whisper, GPT-3, and Stable Diffusion."

"We're thrilled to lead Conway Research’s first round of funding and partner with Sigil as he builds Underdog."

a16z.com/announcement/i…@0xSigil@ConwayResearch@underdogdotai

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a16z's Seema Amble on why AI-native startups still beat incumbents: the incumbent owns the system of...

a16z (@a16z) — Fri, 02 Oct 2026 16:50:24 GMT

a16z's Seema Amble on why AI-native startups still beat incumbents: the incumbent owns the system of record, the startup owns the whole job.

"Why do you need an AI-native startup if you've got Claudeforce... You've got all your data, and your employees are used to the product. So why another product?"

"I absolutely still think there's a case for the AI-native startup, and it centers around the fact that the legacy incumbent is limited to their system of record, and they're not completing the end-to-end job."

"Say a customer calls and says they got charged after they cancelled. Resolving that isn't just the customer going into the chat and saying 'Hey, I got overcharged.'"

"The response there has to hit billing, it has to look at all the chat history, it has to look at the contract. That's not one system of record, that's the knowledge around that customer and everything it touched."

"The opportunity for the AI-native startup is to say: we're going to own that entire end-to-end arc. That could be legal, owning everything from brief all the way through trial... It's really the concept of owning the end-to-end work."

@seema_amble

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a16z@a16z

Lio co-founder and CEO Vladimir Keil joins a16z's Seema Amble and Elena Burger to discuss where AI-native startups have an advantage when incumbent software companies already own the customer, the data, and the system of record:

Their answer comes down to the work that happens outside those systems. In procurement, a final price in an ERP can hide hundreds of emails, spreadsheets, supplier conversations, engineering analyses, and decisions across legal, finance, and operations. Vlad explains how Lio uses multi-agent systems to take on more of that end-to-end work, from sourcing and RFQs to negotiation, shipment tracking, and invoices.

They also discuss how enterprises learn to trust agents with increasingly consequential decisions, why the last 20% of an internal AI build can require most of the effort, and what happens when both buyers and suppliers have agents working on their behalf.

00:58 Why AI startups still beat incumbents
04:19 The hidden work behind an $8K line item
06:18 Retrieval, process, policy, principal
09:27 The incumbent's internal conflict
14:48 What procurement actually looks like
21:13 Procurement at Boeing-scale
28:03 A bolt order, end-to-end
37:57 What a durable vertical AI company looks like
44:46 When both sides deploy agents

YouTube: youtu.be/OTQ-lFsq7zA@askvladi@seema_amble@VirtualElena

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Introducing Discourse: not a group chat, not a broadcast social network, but a secret third thing. ...

a16z (@a16z) — Fri, 02 Oct 2026 16:02:07 GMT

Introducing Discourse: not a group chat, not a broadcast social network, but a secret third thing.

Discourse is built off the Cosign graph and will be embedded into the platform.

discourse.cosign.co

Erik Torenberg@eriktorenberg

Excited to release Discourse, a tech forum for the modern era.    

discourse.cosign.co   

Today, the discourse in tech is either free for all (broadcast social networks) or completely private (group chats). There’s no midsize social network where a few thousand tech leaders talk to each other.    

We'll have a hybrid model. Some people will have access to post directly, and everyone else's posts will go through a queue. We'll start with ~5,000 white-listed people, and more people who post smart comments will be given direct access over time.    

Discourse is built off the @cosign graph and will be embedded into the platform.    

We’ve partnered with our favorite monitors to make sense of what’s happening in real time.    

P.S. True to form, I actually built a version of this a decade ago called @beondeckdaily, inspired by Hacker News.

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Lio co-founder and CEO Vladimir Keil joins a16z's Seema Amble and Elena Burger to discuss where AI-n...

a16z (@a16z) — Fri, 02 Oct 2026 14:37:39 GMT

Lio co-founder and CEO Vladimir Keil joins a16z's Seema Amble and Elena Burger to discuss where AI-native startups have an advantage when incumbent software companies already own the customer, the data, and the system of record:

Their answer comes down to the work that happens outside those systems. In procurement, a final price in an ERP can hide hundreds of emails, spreadsheets, supplier conversations, engineering analyses, and decisions across legal, finance, and operations. Vlad explains how Lio uses multi-agent systems to take on more of that end-to-end work, from sourcing and RFQs to negotiation, shipment tracking, and invoices.

They also discuss how enterprises learn to trust agents with increasingly consequential decisions, why the last 20% of an internal AI build can require most of the effort, and what happens when both buyers and suppliers have agents working on their behalf.

00:58 Why AI startups still beat incumbents
04:19 The hidden work behind an $8K line item
06:18 Retrieval, process, policy, principal
09:27 The incumbent's internal conflict
14:48 What procurement actually looks like
21:13 Procurement at Boeing-scale
28:03 A bolt order, end-to-end
37:57 What a durable vertical AI company looks like
44:46 When both sides deploy agents

YouTube: youtu.be/OTQ-lFsq7zA@askvladi@seema_amble@VirtualElena

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"America's defense-tech renaissance is well underway… Yet raising capital and winning development co...

a16z (@a16z) — Fri, 02 Oct 2026 14:17:49 GMT

"America's defense-tech renaissance is well underway… Yet raising capital and winning development contracts aren't what ultimately matter for great defense companies. What matters is production and fielding at scale."

"Silicon Valley has largely overlooked that base: we haven't funded enough of the manufacturers and supply chain beneath these companies."

"That gap is a national security liability. It is also a generational opportunity for American capital."

"Tier 2 and 3 suppliers, the layer technology hasn't touched, sit grossly underpriced… That is the whitespace. That's the investment opportunity."

"We are entering a new age of manufacturing, one that may be inspired by Silicon Valley, but will be built across Middle America."

@ConnorLoveCA and Collen Larson on The Case for the American Manufacturing Asset Class: a16z.news/p/the-case-for…

Connor Love@ConnorLoveCA

x.com/i/article/2106…

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a16z's David George on product cycles versus capital cycles, and his scorecard for the last 15 years...

a16z (@a16z) — Thu, 01 Oct 2026 22:52:54 GMT

a16z's David George on product cycles versus capital cycles, and his scorecard for the last 15 years of tech:

"AI is unique because if you throw more money at it, it can just scale up and get better. That is different than previous cycles."

"If you threw endless amounts of money at ServiceNow or Workday or Salesforce during the SaaS era, they would get all messed up... Whereas if you put $40 billion towards training these models, they get much better."

"I think about the world as product cycles and capital cycles... In 2021, we were at like a 1 out of 10. We didn't realize it at the time. That was late SaaS, late cloud, post-mobile phone growth... There was no room for something new."

"In 2010 it was like an 8 out of 10. Right now product cycle is like a 9 or 10."

"Your ideal would be a 10 on product cycle and a 10 on capital cycle, meaning valuations are really low. They almost never coincide."

"Like 2023, early days of when AI started working... capital cycle was like a 7... Right now we're probably at a 6. It's okay, but not great."

"But the thing that drives our business over 10 years, whether it's venture or growth, is the product cycles. And that's like a 9 or 10 right now."

@DavidGeorge83 w/ @jaltma

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In venture capital, there are exits, and then there are Exits. 2026 needed its own y-axis. More ch...

a16z (@a16z) — Thu, 01 Oct 2026 21:29:46 GMT

In venture capital, there are exits, and then there are Exits.

2026 needed its own y-axis.

More charts in State of Markets II: a16z.news/p/state-of-mar…

David George@DavidGeorge83

Introducing our State of Markets pt 2, along with a companion podcast where we unpack the data and discuss what comes next.

Tech is the everything cycle.

Supply: putting the buildout in context, just passed railroads as % of GDP. The wisdom of Elon is real: the factory (or the datacenter!) is the product.

Demand: diffusion is so, so early. Median AI vendor spending in the top 1% of companies is 8x that of the top 10%. Only about 30% of S&P 500 companies report a quantified AI impact, which means there’s a substantial opportunity in connecting models to a company's data and workflows. Diffusion into companies is one of the main themes of the next 5 years.

We’re entering the agent work period. Only a few million users today, but applicable to billions of internet users with massive surplus created. META/GOOG monetize US users at $200+ per year today. Agent opportunity is much higher.

Mega-trends the next 5 years: Consumer agents, Robotics, Autonomy, AI x bio, Personal health, Diffusion into enterprise, New era of American Dynamism.

Much more in our SoM report here - a16z.news/p/state-of-mar…@a16z@sarahdingwang@aleximm@santiago__rdz

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a16z(@a16z)

a16z's Alex Immerman, David George, and Sarah Wang on why a data center can lower your electricity b...

The US grid plans to add 86 gigawatts this year. Here's where it's coming from: - Solar: 43.4 GW - ...

.@basepowerco has raised $2.5B to bring affordable, reliable energy to people's homes across America...

We're excited to invest in Preference Model. Building RL environments that actually work is harder ...

Left: Texas's grid only maxes out about 200 hours a year, roughly 2% of the time. Right: New data c...

Texas just froze new data center permits: - Its queue went from 63 GW to 474 GW in 18 months, more ...

"At the end of 2024, Texas' grid operator had 63 GW of large new customers in its queue. By this Jun...

🇯🇵

Instagram turns 16 today

.@Valon just raised a $150M Series D at a $2.3B valuation. Within six months of selling software, th...

Traffic and revenue tell two different stories Replit is a top 10 consumer AI app by spend and a bo...

What are the top 1% of AI spenders paying so much money for? Tools to build, automate, and ship. V...

Greg Brockman of @OpenAI on the new security loop: point every model release at your own systems, fi...

The window to patch software bugs is collapsing Of the bugs hackers actually exploit, ~87% are now ...

Kevin Mandia on why companies need an agent swarm polling their network like a heartbeat, because th...

a16z's Angela Strange sits down with @Valon's Andrew Wang and Linda Du to unpack what it takes to re...

Greg Brockman of @OpenAI on why AI that finds security holes for attackers to exploit is a blessing ...

Cyber is having a moment Across 21 major software companies, including Apple, AWS, Microsoft, and G...

Kevin Mandia on finding 90+ security holes at Fortune 500 companies that nobody knew existed: "When...

We're thrilled to lead General Medicine’s $120M Series B as they build the general store for healthc...

.@ArmadinSecurity is a new company that uses AI agents to attack your network before criminals do, f...

"The top 1% user is spending $903 per month on AI... The median is spending $25." a16z's Olivia Moo...

Which tasks do people actually want AI agents to do? More charts in our Top 100 Consumer AI Apps br...

"Most people aren't looking to save time, they're looking for ways to spend their time." 9 of 15 co...

a16z's Olivia Moore thinks consumer AI has its business model backwards: "Substantially all of the ...

Tweet

13 of the 20 biggest consumer subscription bases are media ChatGPT is the first AI app to crack the...

a16z's Olivia Moore on the untapped category in consumer AI: most people aren't looking to save time...

The top 1% of AI spenders now outspend the bottom 50% combined They spend an average of $903/month,...

The seventh edition of our Top 100 Consumer AI Apps is here, including a new dimension this time: wh...

The seventh edition of our Top 100 Consumer AI Apps is here. New this time: a revenue leaderboard, ...

Ben Horowitz on the rap group he started at 18, and why hip-hop became the source material for how h...

Ben Horowitz on the midnight phone call from Kanye that turned his CEO barbecue into a studio sessio...

Replit CEO Amjad Masad on how general models could train smaller, domain-specific models on the fly:...

.@OpenRouter co-founder Alex Atallah thinks decision models like Jev could be the alignment layer fo...

.@OpenRouter co-founder Alex Atallah on personal agents: 10 specialized chiefs of staff beat one uni...

.@OpenRouter co-founder Alex Atallah on the "everyone is building the same thing" take: "This remin...

.@OpenRouter co-founder Alex Atallah on what convinced him to do the Stripe deal: full autonomy, fas...

.@OpenRouter co-founder Alex Atallah, in his first podcast since Stripe acquired the company, joins ...

America has 16,876 machine shops. 83% employ fewer than 20 people. Just nine employ more than 500. ...

Lio co-founder and CEO Vlad Keil on the KPI for the company's forward-deployed engineers: automate y...

It takes *thousands* of suppliers to build one missile, one aircraft, one ship, or one drone. At th...

This is what happens to a rocket engine when the people who design it and the people who build it si...

"When we first backed Sigil Wen, we knew we were backing an unusually resourceful builder. He seeks ...

a16z's Seema Amble on why AI-native startups still beat incumbents: the incumbent owns the system of...

Introducing Discourse: not a group chat, not a broadcast social network, but a secret third thing. ...

Lio co-founder and CEO Vladimir Keil joins a16z's Seema Amble and Elena Burger to discuss where AI-n...

"America's defense-tech renaissance is well underway… Yet raising capital and winning development co...

a16z's David George on product cycles versus capital cycles, and his scorecard for the last 15 years...

In venture capital, there are exits, and then there are Exits. 2026 needed its own y-axis. More ch...

It takes thousands of suppliers to build one missile, one aircraft, one ship, or one drone. At th...