Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

Lightfield co-founder and CEO Keith Peiris on killing a product with two million monthly users and rebuilding the CRM from scratch: Lightfield's predecessor was an AI presentation tool growing faster than the team could buy inference to support it. Keith shut it down anyway. Nobody on the team liked... the product. Keith decided he wanted to attack the CRM space, a vertical that's notoriously hard to break into. A company's record of its customers is scattered across a whole stack of systems that often contradict each other. His bet is reconciling them into one coherent source of truth, what Lightfield calls a business world model. Every agent a team might run sits downstream of it. In this conversation with a16z's Alex Rampell and Joe Schmidt: the office space they traded for their first 10 customers, why a CRM ended up modeled on the Facebook timeline, three attempts at pricing before one worked, and why nobody at a 40-person company has a fixed role. 00:00 Intro 01:24 Why 2 million users wasn't enough 03:36 Why better models wouldn't have fixed it 06:41 Reconciling the data was the real product 08:16 How free rent bought their first 10 customers 10:12 A CRM does 3 jobs and only 1 matters 11:42 Autopilot CRM nobody has to update 14:56 No more rigid data models 17:11 How one customer modeled a whole marketplace 18:48 Taking customers from an incumbent 23:49 How to survive a new head of sales 26:03 Why the CEO still uses a spreadsheet view 30:55 Pricing models that failed before one worked 36:23 Fixed roles make product pivots impossible 40:02 Why slow shipping loses CRM customers 43:17 What Silicon Valley customers are actually worth 46:20 Why buyers need a reference who looks like them 50:40 What to ignore during a pivot YouTube: Keith Peiris Lightfield Alex Rampell Joe Schmidt IVshow more

a16z

1,076,995 subscribers

114,389 Aufrufe • vor 3 Tagen •via X (Twitter)

Wissenschaft & Technologie

Anya Rossi• Live Now

Private livecam show

22 Kommentare

Profilbild von Blake Fine

Blake Finevor 3 Tagen

legend

Profilbild von Leo Lu

Leo Luvor 3 Tagen

A business world model earns trust when conflicting records stay traceable to their sources.

Profilbild von GrowthwithHonoris

GrowthwithHonorisvor 3 Tagen

2million users can still be the wrong signal. some times the harderdecision is admitting the thing growing fastest isn't the thing worth building.

Profilbild von NameFave

NameFavevor 3 Tagen

one source of truth unlocks everything

Profilbild von kookai · fireply.ai

kookai · fireply.aivor 3 Tagen

killing a growing product on purpose, bold

Profilbild von Chris Suris

Chris Surisvor 3 Tagen

Great update as always. Your insights on bold product decisions bring real value.

Profilbild von Damian Player

Damian Playervor 3 Tagen

bangerrrrr

Profilbild von Abdul Wasey

Abdul Waseyvor 3 Tagen

2 million users can hide a product the team hates. Turning free rent into the first 10 CRM customers says more about founder-market fit than the old growth curve.

Profilbild von The AI Therapist

The AI Therapistvor 3 Tagen

@a16z the classic SaaS pivot. kill a product with distribution to build one without it because seat-based ARR is dying and AI doesn't charge per user anymore. brave move or suicide?

Profilbild von Gaurav Gupta

Gaurav Guptavor 3 Tagen

killing a product with two million users is brutal, but the clue is in the CRM rebuild. presentations are occasional. customer data becomes the daily operating system.

Profilbild von Ali Mirza | AI Agents & Automation

Ali Mirza | AI Agents & Automationvor 2 Tagen

the fatal flaw of modern crms is that they're passive databases relying on manual sales rep data entry. the future belongs to background agents that auto-sync slack, email, and call recordings into state graphs. reconciliation at the ingestion layer is the real product moat.

Profilbild von Người Trẻ Thành Công

Người Trẻ Thành Côngvor 3 Tagen

nói thật, tắt 2 triệu user vì team không thích sản phẩm. Điều ít founder VN dám làm.

Profilbild von Founder Notes

Founder Notesvor 2 Tagen

Killing something with 2M users takes guts. Most teams rationalize keeping the zombie. Curious what the first signal was that made them trust the rebuild more than the growth.

Profilbild von xtamales

xtamalesvor 3 Tagen

Nobody on the team liked the product. == Was a money sink?

Profilbild von Shuohao Liao

Shuohao Liaovor 3 Tagen

The agent layer is downstream of data reconciliation. If customer records disagree, more autonomy just lets the system act on contradictions faster. The source of truth is the hard product.

Profilbild von David Arnal

David Arnalvor 3 Tagen

The interesting shift is treating reconciliation—not data entry—as the CRM product. If the business world model becomes the substrate for every agent, the moat compounds with each workflow instead of each feature.

Profilbild von Vikas(Vik) Malpani| AI for US Real Estate

Vikas(Vik) Malpani| AI for US Real Estatevor 3 Tagen

The tell: it grew faster than they could afford inference, and scale makes that worse, not better. In AI-native products the grow-now-fix-margins-later playbook is inverted. Cost per action is a decision you make before scaling, not after.

Profilbild von Founder Notes

Founder Notesvor 3 Tagen

Killing something with 2M users takes guts. Wonder how they knew the CRM bet was worth the short-term drop.

Profilbild von Sophia Chen

Sophia Chenvor 3 Tagen

Two million monthly users can still be the wrong product if the economics and customer pull don't support it. Killing growth is hard; killing false confidence is harder.

Profilbild von Ava Nakamura

Ava Nakamuravor 2 Tagen

2 million users and they killed it. Usage without pay is not the product.

Profilbild von Odera Joseph

Odera Josephvor 2 Tagen

Two million monthly users is a brutal number to walk away from. When the product grows faster than the team can support, every new feature adds another promise to keep. Rebuilding smaller takes guts.

Profilbild von MASA

MASAvor 3 Tagen

How did investors react to walking away from that traction?

Ähnliche Videos

Lightfield co-founder and CEO Keith Peiris on how they got 10 startups to use a product they didn't ask for: "First, we built a go-to-market assistant. We got people to like it, and then we couldn't get anyone to pay for it... We had all of these AEs using it every day, but we had no pricing power because it wasn't our data." "We decided to shrink the team, start from scratch, and reimagine the CRM from first principles." "We built in the dark for about four months. Then we needed to find people to use our CRM, and it turns out no one wants to use your four-month-old CRM." "We looked at the only asset we had left, which was this giant office space we couldn't get rid of. We were like, 'I'm going to post on X and LinkedIn: You can sit in our office space if you use our CRM.'" "We found 10 startups to come use the product... they were in it every day. They were mad about everything missing. They were mad about how slow it was, but they were in it every day, and they were giving us Slack feedback about it every two hours." "We have this barely working, barely finished product that people are in all the time, where they care about it so much they're going to give us feedback about it on an hourly basis." Keith Peiris Joe Schmidt IV Alex Rampell

Lightfield co-founder and CEO Keith Peiris on how they got 10 startups to use a product they didn't ask for: "First, we built a go-to-market assistant. We got people to like it, and then we couldn't get anyone to pay for it... We had all of these AEs using it every day, but we had no pricing power because it wasn't our data." "We decided to shrink the team, start from scratch, and reimagine the CRM from first principles." "We built in the dark for about four months. Then we needed to find people to use our CRM, and it turns out no one wants to use your four-month-old CRM." "We looked at the only asset we had left, which was this giant office space we couldn't get rid of. We were like, 'I'm going to post on X and LinkedIn: You can sit in our office space if you use our CRM.'" "We found 10 startups to come use the product... they were in it every day. They were mad about everything missing. They were mad about how slow it was, but they were in it every day, and they were giving us Slack feedback about it every two hours." "We have this barely working, barely finished product that people are in all the time, where they care about it so much they're going to give us feedback about it on an hourly basis." Keith Peiris Joe Schmidt IV Alex Rampell

a16z

50,682 Aufrufe • vor 3 Tagen

Lightfield co-founder and CEO Keith Peiris on why AI ends swim lanes and makes everyone a generalist: "One of the things I regret about Tome that we've definitely fixed with Lightfield is... Henry, my co-founder, says this all the time: We had a lot of people playing house." "You have the product leader and the marketing leader and the CS leader, and they all have their swim lanes, and they get really mad if someone gives them feedback about something in their swim lane." "Because of that, we were slowed to a crawl. Maybe put another way, it was impossible to pivot." "So we're like, none of that. No one has a swim lane. Everyone owns product and everyone owns customer success... We have 40 people at the company. Everyone shows up to the same standup every morning. We stack rank the most important problems... and then whoever's free just takes them." "Because of the age that we're in... We have this environment where basically everyone is a generalist. Engineers run projects, designers run projects, CSMs run projects." Keith Peiris Joe Schmidt IV

Lightfield co-founder and CEO Keith Peiris on why AI ends swim lanes and makes everyone a generalist: "One of the things I regret about Tome that we've definitely fixed with Lightfield is... Henry, my co-founder, says this all the time: We had a lot of people playing house." "You have the product leader and the marketing leader and the CS leader, and they all have their swim lanes, and they get really mad if someone gives them feedback about something in their swim lane." "Because of that, we were slowed to a crawl. Maybe put another way, it was impossible to pivot." "So we're like, none of that. No one has a swim lane. Everyone owns product and everyone owns customer success... We have 40 people at the company. Everyone shows up to the same standup every morning. We stack rank the most important problems... and then whoever's free just takes them." "Because of the age that we're in... We have this environment where basically everyone is a generalist. Engineers run projects, designers run projects, CSMs run projects." Keith Peiris Joe Schmidt IV

a16z

35,299 Aufrufe • vor 3 Tagen

Today we're announcing Lightfield's $47M Series A led by a16z to reimagine CRM as a world model of a business. For agents to do customer-facing work, they need to understand how your business actually works. Salesforce wasn't designed for this. It was built 20+ years ago for humans to update records. Layer agentic processes on top and you get low quality output, because the underlying data is incomplete and lacks the structure agents need. Lightfield updates itself from every interaction, forming a trustworthy model of your business for people and agents. Since launching last November, 5,000+ companies have signed up - and we're ripping out legacy CRM at mature organizations with hundreds of users. Companies on Lightfield grow faster than their competitors. It finds prospects that look like your best customers and books meetings with them. It captures commitments from every conversation and automates follow-ups. It diagnoses weaknesses in your funnel and learns from your best sellers to codify what works. The companies of the future will run on a business world model, not a Salesforce-era database. Our mission is to put that capability in the hands of every employee, and every agent, at every company.

Today we're announcing Lightfield's $47M Series A led by a16z to reimagine CRM as a world model of a business. For agents to do customer-facing work, they need to understand how your business actually works. Salesforce wasn't designed for this. It was built 20+ years ago for humans to update records. Layer agentic processes on top and you get low quality output, because the underlying data is incomplete and lacks the structure agents need. Lightfield updates itself from every interaction, forming a trustworthy model of your business for people and agents. Since launching last November, 5,000+ companies have signed up - and we're ripping out legacy CRM at mature organizations with hundreds of users. Companies on Lightfield grow faster than their competitors. It finds prospects that look like your best customers and books meetings with them. It captures commitments from every conversation and automates follow-ups. It diagnoses weaknesses in your funnel and learns from your best sellers to codify what works. The companies of the future will run on a business world model, not a Salesforce-era database. Our mission is to put that capability in the hands of every employee, and every agent, at every company.

Keith Peiris

1,332,407 Aufrufe • vor 10 Tagen

Mike Cannon-Brookes thinks the market is massively underestimating how AI will reshape software. In this conversation with the Atlassian co-founder and CEO, Mike joins a16z's Alex Rampell and Erik Torenberg to cover: - The history of software as “turning filing cabinets into databases” from 1960 to 2022 - The three buckets of SaaS, and why investors keep pricing them like they are the same thing - Why usage and outcome-based pricing is harder than it looks because customers hate bills they cannot control - The real bottleneck for AI at work: trust, UX, and getting humans to actually adopt new workflows 00:00 Introduction 03:16 Risk level has gone up 06:44 Three types of SaaS companies 11:07 Vibecoding is preposterous 14:23 Businesses are a set of processes 31:25 AI credits as casino chips 35:24 How Atlassian is adapting 43:26 Why customer trust is hard Mike Cannon-Brookes 👨🏼‍💻🧢🇦🇺 Alex Rampell Erik Torenberg

Mike Cannon-Brookes thinks the market is massively underestimating how AI will reshape software. In this conversation with the Atlassian co-founder and CEO, Mike joins a16z's Alex Rampell and Erik Torenberg to cover: - The history of software as “turning filing cabinets into databases” from 1960 to 2022 - The three buckets of SaaS, and why investors keep pricing them like they are the same thing - Why usage and outcome-based pricing is harder than it looks because customers hate bills they cannot control - The real bottleneck for AI at work: trust, UX, and getting humans to actually adopt new workflows 00:00 Introduction 03:16 Risk level has gone up 06:44 Three types of SaaS companies 11:07 Vibecoding is preposterous 14:23 Businesses are a set of processes 31:25 AI credits as casino chips 35:24 How Atlassian is adapting 43:26 Why customer trust is hard Mike Cannon-Brookes 👨🏼‍💻🧢🇦🇺 Alex Rampell Erik Torenberg

a16z

154,337 Aufrufe • vor 6 Monaten

Affirm co-founders, CEO Max Levchin and a16z's Alex Rampell, on 25 years inside payments, from PayPal to agentic commerce: Payments is the biggest market on earth. No niche in it is smaller than $100 billion, and the industry has barely moved in 60 years. Visa and Mastercard keep card transaction windows too tight to build anything new inside. Max still calls the plastic card the best payment interface ever built. Affirm began in a 2011 email thread in which Max and Alex agreed that the nearest version of the idea was a bad business. Then a cosmetics brand using Affirm moved the offer from its checkout page to its product pages, and conversion jumped 30%. In this conversation with Erik Torenberg, they get into why Affirm walked away from the highest margins in payments, what a negative customer acquisition cost is actually worth, and why Max is bearish on agentic shopping but bullish on agentic payments. 00:00 Intro 01:00 The fraud rule that built Apple Pay 03:35 Visa's problematic transaction windows 05:30 No niches under $100 billion 07:10 The email that said Affirm was a bad idea 10:00 The wand that should've killed the card 12:36 Booed off stage for presenting PayPal 15:02 Why crypto still can't buy a cup of coffee 17:16 How Max and Alex met 20:11 The 500-Facebook-friends idea 26:48 The all-nighter PHP demo 30:58 Every startup gets 40 years in the desert 32:46 Moving one line lifted conversion 30% 35:02 Why Affirm walked away from the highest margins in payments 39:03 One article created an entire industry 40:37 The asterisk next to 0% 42:17 What BNPL misses about Affirm 44:03 Why merchants give Affirm customers 47:00 Why a 3-year loan is harder, and better, than a 6-week one 48:19 Why PayPal produced so many founders 52:43 Bullish on agentic payments, bearish on agentic shopping 57:51 Grocery shopping is already 100% agentic YouTube: Max Levchin Affirm Alex Rampell Erik Torenberg

a16z

246,462 Aufrufe • vor 16 Tagen

I sat down with Nicolas Sharp, founder of Attio, to talk in depth about how his company is disrupting the $80 billion CRM market. Attio has raised $116m, is 4x'ing ARR and is one of the fastest growing companies in Europe. We talk about: - Why Attio went against all conventional wisdom and spent years 3 years building the product before launching - Why Attio doesn’t hire ‘Software Engineers’ and who they hire instead. - How Attio chose investors who would back a long-term bet against multi-billion $ incumbents - How Attio is building CRM from first principles for the AI era - Who should you avoid hiring at all costs, and who should you hire for your startup And so much more. If you’re interested in learning about the story of how Nick built Attio into the incredible company it is today and is disrupting one of the most important software categories, you’re going to want to see this. Enjoy // Timestamps 00:00 Intro 00:39 Why Attio spent 3 years building their product before launch 5:05 The Power of Building Systems, Not a Box of Features 9:05 How to know when it’s time to launch 13:30 Nick’s Playbook For a Killer Product Launch 18:09 How To Go From an Investor to a Founder 23:05 How Failure Led to Attio's Big Break 28:07 Why startups need to hire "Hidden Gems," 34:05 Fundraising 49:36 Why Attio Invests In Inexperienced Talent 55:07 The Case For Not Hiring Software Engineers (& Who You Should Hire Instead) 1:02:46 The 8 Persona Hiring Framework 1:09:05 How Attio doesn’t use OKR's 1:27:11 Where does Nick's Ambition and Grit come from? 1:36:59 How Attio is Building CRM from First Principles for the AI era 1:45:29 How to Successfully Market In A Crowded Industry 1:51:10 Breaking Down Attio’s Viral Marketing Strategies 2:02:15 The Change That Had The Biggest Impact on Customer Conversion 2:05:04 Why Attio created A “Reverse Trial” 2:10:16 Why Nick is building from London, not Silicon Valley 2:22:48 The 10-Year Vision for Attio

I sat down with Nicolas Sharp, founder of Attio, to talk in depth about how his company is disrupting the $80 billion CRM market. Attio has raised $116m, is 4x'ing ARR and is one of the fastest growing companies in Europe. We talk about: - Why Attio went against all conventional wisdom and spent years 3 years building the product before launching - Why Attio doesn’t hire ‘Software Engineers’ and who they hire instead. - How Attio chose investors who would back a long-term bet against multi-billion $ incumbents - How Attio is building CRM from first principles for the AI era - Who should you avoid hiring at all costs, and who should you hire for your startup And so much more. If you’re interested in learning about the story of how Nick built Attio into the incredible company it is today and is disrupting one of the most important software categories, you’re going to want to see this. Enjoy // Timestamps 00:00 Intro 00:39 Why Attio spent 3 years building their product before launch 5:05 The Power of Building Systems, Not a Box of Features 9:05 How to know when it’s time to launch 13:30 Nick’s Playbook For a Killer Product Launch 18:09 How To Go From an Investor to a Founder 23:05 How Failure Led to Attio's Big Break 28:07 Why startups need to hire "Hidden Gems," 34:05 Fundraising 49:36 Why Attio Invests In Inexperienced Talent 55:07 The Case For Not Hiring Software Engineers (& Who You Should Hire Instead) 1:02:46 The 8 Persona Hiring Framework 1:09:05 How Attio doesn’t use OKR's 1:27:11 Where does Nick's Ambition and Grit come from? 1:36:59 How Attio is Building CRM from First Principles for the AI era 1:45:29 How to Successfully Market In A Crowded Industry 1:51:10 Breaking Down Attio’s Viral Marketing Strategies 2:02:15 The Change That Had The Biggest Impact on Customer Conversion 2:05:04 Why Attio created A “Reverse Trial” 2:10:16 Why Nick is building from London, not Silicon Valley 2:22:48 The 10-Year Vision for Attio

Wouter Teunissen

26,472 Aufrufe • vor 11 Monaten

Gavin Baker and a16z's David George on the state of the AI boom: The future doesn't have to be winner-take-all. Labs, open-source, applications, and the clouds can all capture value. Demand for intelligence is still dramatically underestimated. Today's power users number in the millions and will grow to hundreds of millions. Gavin and David argue a compute shortage is a more real risk than an AI bubble, and building through it is an opportunity to reindustrialize America. In this episode, they get into why compute investments pay back so fast, what the data center backlash gets wrong, the case for putting compute in orbit, why enterprises will run several models at once, and how Nvidia ended up at the center of the entire supply chain. 00:00 Intro 01:06 The bear case Gavin couldn't find 05:50 Why a lab would cut its own revenue 75% 08:05 What LPs get wrong about a crash 10:50 Microsoft slowed its capex and regrets it 14:33 The engineers spending 100x the median 17:35 Why 23-year-olds use AI better than Gavin 21:45 How much copper 500M AI users need 23:00 Stop promising to cure cancer 26:00 America's richest county is full of data centers 30:48 Who gets priced out of compute 33:05 The age of Elon and Jensen 34:25 Orbital data centers 44:40 Asteroid mining 48:12 Why Microsoft doesn't need a frontier model 54:02 Who becomes the abstraction layer 55:40 Everyone wanted a deity, Cursor wanted a product 1:00:25 Never take shots at Jensen 1:07:40 What happens when the chip doesn't work 1:12:10 What chip deals reveal about customer demand YouTube: Gavin Baker David George

Gavin Baker and a16z's David George on the state of the AI boom: The future doesn't have to be winner-take-all. Labs, open-source, applications, and the clouds can all capture value. Demand for intelligence is still dramatically underestimated. Today's power users number in the millions and will grow to hundreds of millions. Gavin and David argue a compute shortage is a more real risk than an AI bubble, and building through it is an opportunity to reindustrialize America. In this episode, they get into why compute investments pay back so fast, what the data center backlash gets wrong, the case for putting compute in orbit, why enterprises will run several models at once, and how Nvidia ended up at the center of the entire supply chain. 00:00 Intro 01:06 The bear case Gavin couldn't find 05:50 Why a lab would cut its own revenue 75% 08:05 What LPs get wrong about a crash 10:50 Microsoft slowed its capex and regrets it 14:33 The engineers spending 100x the median 17:35 Why 23-year-olds use AI better than Gavin 21:45 How much copper 500M AI users need 23:00 Stop promising to cure cancer 26:00 America's richest county is full of data centers 30:48 Who gets priced out of compute 33:05 The age of Elon and Jensen 34:25 Orbital data centers 44:40 Asteroid mining 48:12 Why Microsoft doesn't need a frontier model 54:02 Who becomes the abstraction layer 55:40 Everyone wanted a deity, Cursor wanted a product 1:00:25 Never take shots at Jensen 1:07:40 What happens when the chip doesn't work 1:12:10 What chip deals reveal about customer demand YouTube: Gavin Baker David George

a16z

1,954,293 Aufrufe • vor 19 Tagen

Last month Moderna reported the first individualized cancer vaccine to pass a Phase 3. Moderna CEO Stéphane Bancel and a16z's Jorge Conde on what that actually means: The treatment works by sequencing a patient’s tumor and healthy cells, identifying the mutations relevant to their cancer, and encoding up to 34 of them into an mRNA specific to the individual. Rather than simply unleashing the immune system, the goal is to teach it what to recognize and attack. In this conversation, they unpack the engineering challenge of manufacturing a different medicine for every patient, how Moderna has brought the process down to roughly 42 days from biopsy to treatment, and what it would take to manufacture personalized medicines at scale. 00:00 Intro 02:35 A Phase 3 that beat the standard of care 06:00 Why today's immunotherapy fails 40% of patients 09:35 A vaccine for cancer you already have 11:20 Why mRNA worked where 1,000 trials failed 13:15 90% of cancer antigens are unique to you 16:35 Why this vaccine only gets better from here 21:00 The only thing Moderna needs is your DNA sequence 22:20 Why the first robot was intentionally inefficient 27:10 How the FDA approves a drug that's different every time 33:05 A cancer treatment with the side effects of a flu shot 33:30 Why pancreatic cancer is worth a risk 36:30 Rare liver disease next, then autoimmune YouTube: Moderna Jorge Conde

Last month Moderna reported the first individualized cancer vaccine to pass a Phase 3. Moderna CEO Stéphane Bancel and a16z's Jorge Conde on what that actually means: The treatment works by sequencing a patient’s tumor and healthy cells, identifying the mutations relevant to their cancer, and encoding up to 34 of them into an mRNA specific to the individual. Rather than simply unleashing the immune system, the goal is to teach it what to recognize and attack. In this conversation, they unpack the engineering challenge of manufacturing a different medicine for every patient, how Moderna has brought the process down to roughly 42 days from biopsy to treatment, and what it would take to manufacture personalized medicines at scale. 00:00 Intro 02:35 A Phase 3 that beat the standard of care 06:00 Why today's immunotherapy fails 40% of patients 09:35 A vaccine for cancer you already have 11:20 Why mRNA worked where 1,000 trials failed 13:15 90% of cancer antigens are unique to you 16:35 Why this vaccine only gets better from here 21:00 The only thing Moderna needs is your DNA sequence 22:20 Why the first robot was intentionally inefficient 27:10 How the FDA approves a drug that's different every time 33:05 A cancer treatment with the side effects of a flu shot 33:30 Why pancreatic cancer is worth a risk 36:30 Rare liver disease next, then autoimmune YouTube: Moderna Jorge Conde

a16z

72,421 Aufrufe • vor 17 Tagen

Today, more than 3% of the world's lawyers use Legora, and the company has grown from $1 million to $100 million in ARR since launching in October 2024. At Startup School 2026, Legora co-founder and CEO Max Junestrand shares how they built one of the fastest-growing enterprise software companies in the world, from cold emailing lawyers and moving into a customer's office to freezing sales for six months to rebuild the product. He explains why building a company is ultimately about people, how to create a culture that wants to win, and why founders have to learn to love the hustle. 00:07 — From 3 Engineers to $100M ARR 03:15 — How Legora Got Started 05:09 — Learning the Legal Industry From Scratch 07:22 — Getting Rejected by YC 09:36 — Moving Into a Customer's Office 11:11 — Going From Zero to $1M ARR During YC 13:26 — Why We Froze Sales for Six Months 15:18 — Rebuilding the Product From Scratch 17:26 — Building a Company Is About People 21:51 — Creating a Culture That Wants to Win 25:26 — You Have to Love the Hustle 27:55 — Do Founders Need Domain Expertise? 29:14 — Betting on Models Getting Better 36:29 — When Should You Start a Company? 45:04 — How to Become More Ambitious 48:18 — The Skills Founders Need Today

Today, more than 3% of the world's lawyers use Legora, and the company has grown from $1 million to $100 million in ARR since launching in October 2024. At Startup School 2026, Legora co-founder and CEO Max Junestrand shares how they built one of the fastest-growing enterprise software companies in the world, from cold emailing lawyers and moving into a customer's office to freezing sales for six months to rebuild the product. He explains why building a company is ultimately about people, how to create a culture that wants to win, and why founders have to learn to love the hustle. 00:07 — From 3 Engineers to $100M ARR 03:15 — How Legora Got Started 05:09 — Learning the Legal Industry From Scratch 07:22 — Getting Rejected by YC 09:36 — Moving Into a Customer's Office 11:11 — Going From Zero to $1M ARR During YC 13:26 — Why We Froze Sales for Six Months 15:18 — Rebuilding the Product From Scratch 17:26 — Building a Company Is About People 21:51 — Creating a Culture That Wants to Win 25:26 — You Have to Love the Hustle 27:55 — Do Founders Need Domain Expertise? 29:14 — Betting on Models Getting Better 36:29 — When Should You Start a Company? 45:04 — How to Become More Ambitious 48:18 — The Skills Founders Need Today

Y Combinator

78,443 Aufrufe • vor 24 Tagen

Today, a16z is announcing the Machine Age Fund, a new $1.1 billion fund for founders rebuilding what intelligence runs on: chips, memory, networking, systems software, power, and the machines that bring AI into the physical world. Ben Horowitz, Martin Casado, and Raghu Raghuram see a new economic law. A thousand engineers cannot erase a two-year software lead, but a massive GPU cluster can turn capital directly into capability. Models are improving faster than the memory, interconnect, power, and cooling beneath them. The founder map is changing with it. Some of the strongest teams are moving from pure software into complex hardware because every constraint in the stack is now a company-building opportunity. In this conversation with Erik Torenberg, they explain what founders can build, why the opportunity reaches all the way down to the physical stack, and why a16z created a fund for it. 00:00 Intro 01:08 Why AI needs an entirely new infrastructure 02:02 The bottleneck is no longer the model 02:40 Founders saw it before investors did 06:07 Sold out through 2028 07:14 Why this time is different from 1999 08:57 The company whose idle hardware gained value 10:51 "Why didn't this fund exist five years ago?" 13:51 "Nobody likes to use AI more than AI" 17:06 The startup law that capital just broke 21:05 What Grok Bot got right 26:43 The case for a custom chip per model 28:23 Why AC power isn't good enough 32:07 Lots of new electricians 34:14 What one gigawatt can power 35:03 Why utilities can't just build faster 37:22 The data centers that give power back 38:01 Why we should never have called it AI 39:43 Why Nvidia will willingly leave money on the table 48:08 Hardware founders are older 52:36 Why America has to lead YouTube: benahorowitz.eth Raghu Raghuram martin_casado Erik Torenberg

Today, a16z is announcing the Machine Age Fund, a new $1.1 billion fund for founders rebuilding what intelligence runs on: chips, memory, networking, systems software, power, and the machines that bring AI into the physical world. Ben Horowitz, Martin Casado, and Raghu Raghuram see a new economic law. A thousand engineers cannot erase a two-year software lead, but a massive GPU cluster can turn capital directly into capability. Models are improving faster than the memory, interconnect, power, and cooling beneath them. The founder map is changing with it. Some of the strongest teams are moving from pure software into complex hardware because every constraint in the stack is now a company-building opportunity. In this conversation with Erik Torenberg, they explain what founders can build, why the opportunity reaches all the way down to the physical stack, and why a16z created a fund for it. 00:00 Intro 01:08 Why AI needs an entirely new infrastructure 02:02 The bottleneck is no longer the model 02:40 Founders saw it before investors did 06:07 Sold out through 2028 07:14 Why this time is different from 1999 08:57 The company whose idle hardware gained value 10:51 "Why didn't this fund exist five years ago?" 13:51 "Nobody likes to use AI more than AI" 17:06 The startup law that capital just broke 21:05 What Grok Bot got right 26:43 The case for a custom chip per model 28:23 Why AC power isn't good enough 32:07 Lots of new electricians 34:14 What one gigawatt can power 35:03 Why utilities can't just build faster 37:22 The data centers that give power back 38:01 Why we should never have called it AI 39:43 Why Nvidia will willingly leave money on the table 48:08 Hardware founders are older 52:36 Why America has to lead YouTube: benahorowitz.eth Raghu Raghuram martin_casado Erik Torenberg

a16z

640,299 Aufrufe • vor 22 Tagen

Most founders accidentally train their customers never to pay them. Madhavan Ramanujam (the "pricing guru" of Silicon Valley - having worked with LinkedIn, Uber, and 30+ unicorns) calls it the 20/80 Pricing Trap • 20% of your features drive 80% of the willingness to pay • Founders give that 20% away for free to gain distribution • You are left trying to monetize the remaining 80% of features - the ones users don't actually value. The result? You build a charity, not a business. In this week's episode of The Library of Minds, we discuss the science of monetization and deconstruct how to architect ‘Profitable Growth’ - the core framework from his new book, Scaling Innovation. 03:33 - Netflix vs Blockbuster: The Pricing Decision That Changed Tech 08:06 - Why Most Startups Get Pricing Wrong 11:39 - Freemium vs Paid: When Free Destroys Value 15:38 - Pricing Models Matter More Than Price 16:28 - The AI Pricing Framework: Autonomy vs Attribution 21:49 - The Biggest Pricing Mistake Ever 25:05 - Why Steve Jobs Was a Pricing Genius 26:44 - Behavioral Pricing: How Founders 10× Deals Without Changing Product 31:02 - Data vs Conviction: How Great Founders Make Pricing Decisions

Most founders accidentally train their customers never to pay them. Madhavan Ramanujam (the "pricing guru" of Silicon Valley - having worked with LinkedIn, Uber, and 30+ unicorns) calls it the 20/80 Pricing Trap • 20% of your features drive 80% of the willingness to pay • Founders give that 20% away for free to gain distribution • You are left trying to monetize the remaining 80% of features - the ones users don't actually value. The result? You build a charity, not a business. In this week's episode of The Library of Minds, we discuss the science of monetization and deconstruct how to architect ‘Profitable Growth’ - the core framework from his new book, Scaling Innovation. 03:33 - Netflix vs Blockbuster: The Pricing Decision That Changed Tech 08:06 - Why Most Startups Get Pricing Wrong 11:39 - Freemium vs Paid: When Free Destroys Value 15:38 - Pricing Models Matter More Than Price 16:28 - The AI Pricing Framework: Autonomy vs Attribution 21:49 - The Biggest Pricing Mistake Ever 25:05 - Why Steve Jobs Was a Pricing Genius 26:44 - Behavioral Pricing: How Founders 10× Deals Without Changing Product 31:02 - Data vs Conviction: How Great Founders Make Pricing Decisions

Dara

215,786 Aufrufe • vor 9 Monaten

Board member of Coinbase, Pinterest, & TradeDesk, Gokul Rajaram, thinks credentials may be worthless in the AI era. The man who built Google AdSense from 0 to $1 billion has watched ex-FAANG PMs and GTM leaders crash and burn in AI-native startups. First principles thinkers with no resume are leaving them in the dust. In this week's episode, we dig into: • Why traditional product instincts break in AI • How to hire for velocity & curiosity, not credentials • Why customer relationships are the new moat 00:00 - Intro 01:00 - Who is Gokul 03:10 – Building Google’s Ad Model 06:00 – Metrics That Matter 07:50 – When to Ignore the Data 10:10 – Prioritizing Customers & Segments 11:50 – Knowing When to Kill a Product 13:30 – When to Build Product #2 18:03 – Hardest Moment in Gokul’s Career 20:20 – Founder Paranoia & Retention Metrics 22:00 – Culture & Early Team Dynamics 25:50 – What Makes a Team Scale 30:00 – Lessons from Great Leaders 33:40 – Sheryl Sandberg’s Decision Framework

Board member of Coinbase, Pinterest, & TradeDesk, Gokul Rajaram, thinks credentials may be worthless in the AI era. The man who built Google AdSense from 0 to $1 billion has watched ex-FAANG PMs and GTM leaders crash and burn in AI-native startups. First principles thinkers with no resume are leaving them in the dust. In this week's episode, we dig into: • Why traditional product instincts break in AI • How to hire for velocity & curiosity, not credentials • Why customer relationships are the new moat 00:00 - Intro 01:00 - Who is Gokul 03:10 – Building Google’s Ad Model 06:00 – Metrics That Matter 07:50 – When to Ignore the Data 10:10 – Prioritizing Customers & Segments 11:50 – Knowing When to Kill a Product 13:30 – When to Build Product #2 18:03 – Hardest Moment in Gokul’s Career 20:20 – Founder Paranoia & Retention Metrics 22:00 – Culture & Early Team Dynamics 25:50 – What Makes a Team Scale 30:00 – Lessons from Great Leaders 33:40 – Sheryl Sandberg’s Decision Framework

Dara

377,312 Aufrufe • vor 10 Monaten

In third grade, Grant LaFontaine sold a Pokemon card online. The buyer mailed a money order; Grant cashed it at the post office and then shipped the card to the customer. Today he's co-founder and CEO of Whatnot, where the largest businesses do over $100 million a year at up to 40% margins. Grant joins a16z's David George to discuss how that first transaction became a platform that did $8 billion in sales last year, why live commerce is a third of all commerce in China yet still emerging in the US, and how Whatnot operates reliably at scale, and more. 00:00 Intro 01:05 The Pokemon card and the money order 06:25 Half a Bitcoin for a domain 09:20 Why not knowing the market was an advantage 11:05 Customers don't care about your market 12:45 Most people don't buy anything 17:15 Why the eBay comparison misses 20:05 95 minutes a day on a shopping app 25:00 Sellers doing $100M with 40% margins 26:35 Fresh fish from the San Diego dock 32:25 Running a police force for two New York Cities 37:00 Why Whatnot won't do AI avatars 40:15 Cars on Whatnot Grant LaFontaine David George

In third grade, Grant LaFontaine sold a Pokemon card online. The buyer mailed a money order; Grant cashed it at the post office and then shipped the card to the customer. Today he's co-founder and CEO of Whatnot, where the largest businesses do over $100 million a year at up to 40% margins. Grant joins a16z's David George to discuss how that first transaction became a platform that did $8 billion in sales last year, why live commerce is a third of all commerce in China yet still emerging in the US, and how Whatnot operates reliably at scale, and more. 00:00 Intro 01:05 The Pokemon card and the money order 06:25 Half a Bitcoin for a domain 09:20 Why not knowing the market was an advantage 11:05 Customers don't care about your market 12:45 Most people don't buy anything 17:15 Why the eBay comparison misses 20:05 95 minutes a day on a shopping app 25:00 Sellers doing $100M with 40% margins 26:35 Fresh fish from the San Diego dock 32:25 Running a police force for two New York Cities 37:00 Why Whatnot won't do AI avatars 40:15 Cars on Whatnot Grant LaFontaine David George

a16z

50,425 Aufrufe • vor 1 Monat

University of Toronto mathematician Daniel Litt and a16z's Lisha Li on AI's impact on mathematics: The models are good at a narrower slice of math than the headlines suggest. They grind long computations, pull technical ideas from more papers than any human could read, and apply every known technique better than almost anyone. What they don't do is build theory, or hold a vague philosophy long enough to make it precise, which is most of what Daniel says he actually does for a living. In this conversation, he and Lisha get into how mathematicians raided an AI proof for parts and broke several other problems with them, why a thousand AI mathematicians might all turn out to be the same mathematician, and why the proof a model handed Daniel was correct but still worth nothing. 00:00 Intro 02:10 The Erdős problem AI disproved 06:20 AI's reasoning looks recognizably human 07:55 Why English beat formal proofs 10:00 Why models can't build theory 14:50 Open problems measure your ignorance 17:45 How a graph became a Millennium Prize problem 18:58 Where AI doesn't help Daniel 21:15 Why ugly proofs are worth doing 23:42 True conjectures are harder than false ones 29:32 10 pages of calculation, zero insight 34:55 The goal of math is not to produce papers 36:25 5 conjectures, 3 bad papers, 1 hour 38:05 One mathematician duplicated 1000x 40:48 Why humans matter even if models win 46:30 When cheaper and worse beats better 49:22 Why the newest AI result isn't a big deal 57:05 How mathematicians actually check a long proof 59:38 Daniel's 3-year-old is already doing math YouTube: Daniel Litt Lisha

University of Toronto mathematician Daniel Litt and a16z's Lisha Li on AI's impact on mathematics: The models are good at a narrower slice of math than the headlines suggest. They grind long computations, pull technical ideas from more papers than any human could read, and apply every known technique better than almost anyone. What they don't do is build theory, or hold a vague philosophy long enough to make it precise, which is most of what Daniel says he actually does for a living. In this conversation, he and Lisha get into how mathematicians raided an AI proof for parts and broke several other problems with them, why a thousand AI mathematicians might all turn out to be the same mathematician, and why the proof a model handed Daniel was correct but still worth nothing. 00:00 Intro 02:10 The Erdős problem AI disproved 06:20 AI's reasoning looks recognizably human 07:55 Why English beat formal proofs 10:00 Why models can't build theory 14:50 Open problems measure your ignorance 17:45 How a graph became a Millennium Prize problem 18:58 Where AI doesn't help Daniel 21:15 Why ugly proofs are worth doing 23:42 True conjectures are harder than false ones 29:32 10 pages of calculation, zero insight 34:55 The goal of math is not to produce papers 36:25 5 conjectures, 3 bad papers, 1 hour 38:05 One mathematician duplicated 1000x 40:48 Why humans matter even if models win 46:30 When cheaper and worse beats better 49:22 Why the newest AI result isn't a big deal 57:05 How mathematicians actually check a long proof 59:38 Daniel's 3-year-old is already doing math YouTube: Daniel Litt Lisha

a16z

143,631 Aufrufe • vor 18 Tagen

NEW: Inside AI's Biggest Downstream Winner.. the Surge in AI Database Demand "Data is the unsung hero, & data is back." MongoDB CEO CJ Desai (CJ Desai) The unexpected result? Hyperscalers are turning away even top-50 accounts. "Sorry, we don't have a capacity." "And they are one of the top 50 customers for that hyperscaler." ElevenLabs alone runs "north of 50 million agents, depending on when you look at it, all running on MongoDB.. that gives us a lot of confidence that we have the right architecture for agentic workloads." NASDAQ: $MDB We cover: › The 3 classes of AI customers MongoDB serves › Why hyperscalers are telling top-50 accounts "no capacity" › On-prem, sovereign AI, and the data-center comeback › ElevenLabs running 50M+ agents on MongoDB › MongoDB (OLTP) vs Snowflake & Databricks (OLAP) › Why there is no standardization in enterprise AI models › Auto-scaling & the fall of expensive DBAs 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) CJ Desai, CEO at MongoDB (01:12) Top CEOs at the Raise AI Summit (03:24) The 3 types of companies powering the AI boom (07:25) Why on-prem is making a shocking comeback (09:01) Hyperscalers are quietly running out of capacity (11:06) What actually separates MongoDB from Snowflake x Databricks (13:18) Why Frontier Labs treat MongoDB as their memory layer (16:22) From Oracle intern to first-time CEO (19:19) Becoming CEO during the AI chaos (23:53) The World Cup crisis that tested MongoDB's scale (28:19) The real complexity behind simple AI agents (31:22) Open source vs. closed models: what customers actually pick (34:36) CJ's honest take on data centers in space (36:50) The mentors who shaped a first-time CEO (40:32) MongoDB's next big bets (41:36) Data is back?

NEW: Inside AI's Biggest Downstream Winner.. the Surge in AI Database Demand "Data is the unsung hero, & data is back." MongoDB CEO CJ Desai (CJ Desai) The unexpected result? Hyperscalers are turning away even top-50 accounts. "Sorry, we don't have a capacity." "And they are one of the top 50 customers for that hyperscaler." ElevenLabs alone runs "north of 50 million agents, depending on when you look at it, all running on MongoDB.. that gives us a lot of confidence that we have the right architecture for agentic workloads." NASDAQ: $MDB We cover: › The 3 classes of AI customers MongoDB serves › Why hyperscalers are telling top-50 accounts "no capacity" › On-prem, sovereign AI, and the data-center comeback › ElevenLabs running 50M+ agents on MongoDB › MongoDB (OLTP) vs Snowflake & Databricks (OLAP) › Why there is no standardization in enterprise AI models › Auto-scaling & the fall of expensive DBAs 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) CJ Desai, CEO at MongoDB (01:12) Top CEOs at the Raise AI Summit (03:24) The 3 types of companies powering the AI boom (07:25) Why on-prem is making a shocking comeback (09:01) Hyperscalers are quietly running out of capacity (11:06) What actually separates MongoDB from Snowflake x Databricks (13:18) Why Frontier Labs treat MongoDB as their memory layer (16:22) From Oracle intern to first-time CEO (19:19) Becoming CEO during the AI chaos (23:53) The World Cup crisis that tested MongoDB's scale (28:19) The real complexity behind simple AI agents (31:22) Open source vs. closed models: what customers actually pick (34:36) CJ's honest take on data centers in space (36:50) The mentors who shaped a first-time CEO (40:32) MongoDB's next big bets (41:36) Data is back?

Molly O’Shea

170,786 Aufrufe • vor 2 Monaten

For 30 years, nobody really cared about backup. It was IT plumbing - a compliance checkbox, something you set up and never touched again. The last place you'd expect a founder to go in 2024, at the peak of the AI boom. Which is exactly when Ofir Ehrlich - who sold CloudEndure to Amazon - decided to start @Eon_io_inc. Because it turns out backup is one of the most important surfaces in enterprise AI. Every version of every record a company has ever created sits there, and it's locked in the archive nobody wanted to own. He joined us on Lightwork to explain what Eon is building: 🚀 His path as a 4x founder and why he’s building with his best friends (including a co-founder of 26 years he avoided working with until the co-founder's mom overruled them both) 🖥️ Why "the cloud is just someone else's computer" - and who's actually on the hook when things go wrong 💸 The 10-30% of billion-dollar cloud bills that nobody inside the enterprise is even accountable for 🗄️ Why retrieving one 5 year old record can take a 30 person SWAT team - and why some enterprises have teams of hundreds doing only this 🤖 How Eon built a backup product and kept getting forwarded to the AI team 🔒 The real blocker on enterprise AI: owners scared of what a model might surface once it can query their data Full episode on your favorite podcast platform 🎧 Lightspeed

For 30 years, nobody really cared about backup. It was IT plumbing - a compliance checkbox, something you set up and never touched again. The last place you'd expect a founder to go in 2024, at the peak of the AI boom. Which is exactly when Ofir Ehrlich - who sold CloudEndure to Amazon - decided to start @Eon_io_inc. Because it turns out backup is one of the most important surfaces in enterprise AI. Every version of every record a company has ever created sits there, and it's locked in the archive nobody wanted to own. He joined us on Lightwork to explain what Eon is building: 🚀 His path as a 4x founder and why he’s building with his best friends (including a co-founder of 26 years he avoided working with until the co-founder's mom overruled them both) 🖥️ Why "the cloud is just someone else's computer" - and who's actually on the hook when things go wrong 💸 The 10-30% of billion-dollar cloud bills that nobody inside the enterprise is even accountable for 🗄️ Why retrieving one 5 year old record can take a 30 person SWAT team - and why some enterprises have teams of hundreds doing only this 🤖 How Eon built a backup product and kept getting forwarded to the AI team 🔒 The real blocker on enterprise AI: owners scared of what a model might surface once it can query their data Full episode on your favorite podcast platform 🎧 Lightspeed

Claire Zau

21,911 Aufrufe • vor 1 Monat

Christina Cacioppo (Christina Cacioppo) is the co-founder and CEO of Vanta, the trust management platform helping 12,000+ companies prove security and compliance. She’s scaled the company from a spreadsheet prototype to a global team of 1,000, most recently raising at a $4.15B valuation. On the Social Radars, Christina shares how she trusted her gut to make unconventional decisions, the lessons she’s learned on delegation and focus, and what it means to stay in founder mode while building one of the fastest-growing companies in SaaS. 00:25 – From Spreadsheet Prototype to Platform 02:00 – Why Security and Compliance Were Broken 04:00 – Convincing the First Customers 06:00 – Scaling to 12,000+ Companies 08:30 – Lessons on Gut Instincts & Leadership 10:30 – Raising at a $4.15B Valuation 12:00 – Staying in Founder Mode

Christina Cacioppo (Christina Cacioppo) is the co-founder and CEO of Vanta, the trust management platform helping 12,000+ companies prove security and compliance. She’s scaled the company from a spreadsheet prototype to a global team of 1,000, most recently raising at a $4.15B valuation. On the Social Radars, Christina shares how she trusted her gut to make unconventional decisions, the lessons she’s learned on delegation and focus, and what it means to stay in founder mode while building one of the fastest-growing companies in SaaS. 00:25 – From Spreadsheet Prototype to Platform 02:00 – Why Security and Compliance Were Broken 04:00 – Convincing the First Customers 06:00 – Scaling to 12,000+ Companies 08:30 – Lessons on Gut Instincts & Leadership 10:30 – Raising at a $4.15B Valuation 12:00 – Staying in Founder Mode

Y Combinator

55,711 Aufrufe • vor 11 Monaten

🎧🍌 New The Peel with Ankur Goyal Ankur is the Founder and CEO of @braintrustdata, the end to end developer platform for building the world's best AI products. Their customers include companies like Instacart, Zapier, Notion, Airtable, Replit, and more. We hit on the importance of LLM evals, advice for building AI products, why the best companies have two AI product roadmaps, how he hates meetings, and all his non-conventional advice for founders. Watch below or links in the replies! Timestamps: 04:04 Why everyone’s now an AI company 06:03 Reasons LLM evals are so important 09:19 Replacing vibe checks with Braintrust 10:37 Making OpenAI’s protocols the standard 11:27 Why the best companies have two AI roadmaps 13:06 Build your product so each LLM release makes it better 14:54 Predicting AGI is impossible 15:54 Why people who work with LLMs aren’t worried about AI safety 16:52 The best developers are all-in on co-pilots 18:11 How AI is changing software development 21:09 Combining IDE, CI/DC, and observability in one product 27:18 Are models more like CPU’s or relational databases? 30:14 How to pick an LLM 33:00 Advice for staying on top of new AI developments 34:30 Why tool calling is so important 38:02 Advice for young software engineers 40:25 Learning to code doing linear algebra homework 42:36 Lack of purpose interning in big tech 44:07 Working at MemSQL learning to be a founder 47:52 How to get a job at a startup 50:43 Building his first startups product on an flight 52:39 Three lessons from his first failed startup 54:46 Don’t delegate what you’re good at 55:46 Why you should be careful listening to VCs advice 57:34 Tactics for successful delegation 59:36 Why Ankur hates meetings 1:02:42 The importance of self-service in unlocking certain customer segments 1:05:14 How Braintrust got started 1:07:45 Advice on picking your target customers 1:10:35 How Braintrust hires with work trials 1:15:21 Balancing security with a modern UI 1:17:49 Why it’s hard to sell non-AI products right now 1:19:21 Advice for selling to large enterprises 1:23:10 Ankur’s favorite AI products

🎧🍌 New The Peel with Ankur Goyal Ankur is the Founder and CEO of @braintrustdata, the end to end developer platform for building the world's best AI products. Their customers include companies like Instacart, Zapier, Notion, Airtable, Replit, and more. We hit on the importance of LLM evals, advice for building AI products, why the best companies have two AI product roadmaps, how he hates meetings, and all his non-conventional advice for founders. Watch below or links in the replies! Timestamps: 04:04 Why everyone’s now an AI company 06:03 Reasons LLM evals are so important 09:19 Replacing vibe checks with Braintrust 10:37 Making OpenAI’s protocols the standard 11:27 Why the best companies have two AI roadmaps 13:06 Build your product so each LLM release makes it better 14:54 Predicting AGI is impossible 15:54 Why people who work with LLMs aren’t worried about AI safety 16:52 The best developers are all-in on co-pilots 18:11 How AI is changing software development 21:09 Combining IDE, CI/DC, and observability in one product 27:18 Are models more like CPU’s or relational databases? 30:14 How to pick an LLM 33:00 Advice for staying on top of new AI developments 34:30 Why tool calling is so important 38:02 Advice for young software engineers 40:25 Learning to code doing linear algebra homework 42:36 Lack of purpose interning in big tech 44:07 Working at MemSQL learning to be a founder 47:52 How to get a job at a startup 50:43 Building his first startups product on an flight 52:39 Three lessons from his first failed startup 54:46 Don’t delegate what you’re good at 55:46 Why you should be careful listening to VCs advice 57:34 Tactics for successful delegation 59:36 Why Ankur hates meetings 1:02:42 The importance of self-service in unlocking certain customer segments 1:05:14 How Braintrust got started 1:07:45 Advice on picking your target customers 1:10:35 How Braintrust hires with work trials 1:15:21 Balancing security with a modern UI 1:17:49 Why it’s hard to sell non-AI products right now 1:19:21 Advice for selling to large enterprises 1:23:10 Ankur’s favorite AI products

Turner Novak 🍌🧢

44,280 Aufrufe • vor 2 Jahren

I had an incredible chat with se, co-founder of fomo (now @fomo). fomo launched less than a year ago but has already had a remarkable rise, becoming a go-to trading app for both crypto-native and mainstream users alike. We cover a lot of ground, from the original motivation to build fomo, to recruiting the early team, what worked (and didn't work) with user acquisition, and what he's learned over the past year. Sometimes the success behind certain crypto products can feel random, but it's clear that fomo's is not. They've been extremely intentional with every part of the company (team/product/growth/fundraising), and it reflects in the feedback from users. Was a pleasure recording this one. (0:00) Intro (1:13) Early life. Jersey and Yale (7:03) Working on product at dYdX and meeting Paul (14:06) The genesis of fomo, early team formation (33:42) Product-first philosophy and obsession with UX (46:24) User-led growth and getting to know your customers (59:48) Raising Series A from Benchmark (1:06:22) Where fomo is heading; the speculation graph era (1:21:49) Building a social product to match its growth (1:28:58) Memecoins as a category (1:40:35) Other teams that Se admires

I had an incredible chat with se, co-founder of fomo (now @fomo). fomo launched less than a year ago but has already had a remarkable rise, becoming a go-to trading app for both crypto-native and mainstream users alike. We cover a lot of ground, from the original motivation to build fomo, to recruiting the early team, what worked (and didn't work) with user acquisition, and what he's learned over the past year. Sometimes the success behind certain crypto products can feel random, but it's clear that fomo's is not. They've been extremely intentional with every part of the company (team/product/growth/fundraising), and it reflects in the feedback from users. Was a pleasure recording this one. (0:00) Intro (1:13) Early life. Jersey and Yale (7:03) Working on product at dYdX and meeting Paul (14:06) The genesis of fomo, early team formation (33:42) Product-first philosophy and obsession with UX (46:24) User-led growth and getting to know your customers (59:48) Raising Series A from Benchmark (1:06:22) Where fomo is heading; the speculation graph era (1:21:49) Building a social product to match its growth (1:28:58) Memecoins as a category (1:40:35) Other teams that Se admires

Derek Hsue

39,225 Aufrufe • vor 5 Monaten

The AI era of apps is here. In this discussion with a16z Apps Fund GPs Alex Rampell, David Haber, and Anish Acharya, they break down: - How AI is driving explosive product growth - The new product categories that couldn't exist before AI - Why AI-native startups should look for “greenfield” opportunities with unserved customers instead of competing with large incumbents - Why moats still matter - How software is eating labor 00:00 Introduction 02:17 AI's role in enterprise and consumer applications 05:03 Emerging AI trends and investment strategies 08:43 Traditional software going AI-native 14:40 Software eating labor 17:04 Case study: Eve 21:33 Building defensible moats 24:45 Case study: Salient 31:53 The walled garden 40:23 Incumbents vs startups 53:32 Consumer AI applications 56:03 Model aggregation strategy 57:06 Investment process & team 1:06:46 Customer retention & enterprise sales Alex Rampell David Haber Anish Acharya

The AI era of apps is here. In this discussion with a16z Apps Fund GPs Alex Rampell, David Haber, and Anish Acharya, they break down: - How AI is driving explosive product growth - The new product categories that couldn't exist before AI - Why AI-native startups should look for “greenfield” opportunities with unserved customers instead of competing with large incumbents - Why moats still matter - How software is eating labor 00:00 Introduction 02:17 AI's role in enterprise and consumer applications 05:03 Emerging AI trends and investment strategies 08:43 Traditional software going AI-native 14:40 Software eating labor 17:04 Case study: Eve 21:33 Building defensible moats 24:45 Case study: Salient 31:53 The walled garden 40:23 Incumbents vs startups 53:32 Consumer AI applications 56:03 Model aggregation strategy 57:06 Investment process & team 1:06:46 Customer retention & enterprise sales Alex Rampell David Haber Anish Acharya

a16z

87,765 Aufrufe • vor 8 Monaten