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Introducing Cosign.
cosign.co
Excited to introduce @Cosign: the curated professional network for the startup community: cosign.co
Our goal is to create the following:
- A comprehensive startup directory of investors, companies, and operators, including what they worked on and who they worked on it with.
- Cosign graph: Who shaped your career? Who were you actually in the trenches with? Who would work with you again? Who thinks you’re someone to watch?
- Durable reputation: Great endorsements happen every day on X and disappear into the feed. Cosign attaches those signals permanently to people and companies.
- Discovery: Who are the best fintech angels? Which AI companies should I watch? Who are the best designers?
- Intent network: Privately signal “I’d invest in them,” “I’d hire them,” “I’d work with them,” or eventually even “I’d acquire this company.” Match people when interest exists on both sides.
Imagine if Wikipedia, LinkedIn, and OG AngelList had a baby. Though Cosign is a community, not a business.
In order to join, you need to be cosigned. Or you can apply directly.
David Booth and I started this idea 7 years ago but didn’t have the firepower to make it work. Now we do.
No one has really touched LinkedIn in 20 years. Excited to take a swing.
cosign.co
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
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
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
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
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
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
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
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
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:
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 👇
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
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
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
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
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
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
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
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
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
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
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
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
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
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-cha…
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-cha…
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
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
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-cha…
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
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
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
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-cha…
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
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