Aish

@aish_caliperce • 4,367 subscribers

Early stage, PMF, and upmarket stories · @jointhebridge (Part of Entrepreneurs First) · ex-@hackerrank · 19

Videos

Anya Rossi

sweetdream.ai

SweetDream.ai•Sponsored•Livecam

Watch Anya Live

Anya is streaming live right now! Join her private show and enjoy exclusive content.

Exclusive private shows

1.2k viewers online

Private Show

Join now for exclusive access

Free preview available • Premium content

"Every former Nvidia employee that goes to a large tech company like Google and MSFT says it's extremely difficult to adjust to the culture, Nvidia is faster but the work ethic is on a different level" 3dfx was the No 1 graphics company in the mid-to-late 90s, their main competitor Nvidia beat them and essentially destroyed them - acquired their assets and hired 100 of their engineers. The 3dfx engineers kept looking for Nvidia's secret or magic trick, but it turned out the only secret was relentless execution & working crazy hard. This culture obviously stems from the CEO, Jensen - who loves to work on Sunday nights & finds it relaxing. Jensen is constantly focused on cultivating talent - he rewards highly performing engineers by doubling or tripling their stock compensation. To avoid the internal politics that plague large organizations (where projects get stuck in quicksand needing multiple approvals), Jensen made the organization flat. Every time they need to make decisions, he brings together top 10-15 people, including lower-level employees, to hash out the main problems in an intellectually honest way and execute quickly - what they call "speed of light" at Nvidia.

"Every former Nvidia employee that goes to a large tech company like Google and MSFT says it's extremely difficult to adjust to the culture, Nvidia is faster but the work ethic is on a different level" 3dfx was the No 1 graphics company in the mid-to-late 90s, their main competitor Nvidia beat them and essentially destroyed them - acquired their assets and hired 100 of their engineers. The 3dfx engineers kept looking for Nvidia's secret or magic trick, but it turned out the only secret was relentless execution & working crazy hard. This culture obviously stems from the CEO, Jensen - who loves to work on Sunday nights & finds it relaxing. Jensen is constantly focused on cultivating talent - he rewards highly performing engineers by doubling or tripling their stock compensation. To avoid the internal politics that plague large organizations (where projects get stuck in quicksand needing multiple approvals), Jensen made the organization flat. Every time they need to make decisions, he brings together top 10-15 people, including lower-level employees, to hash out the main problems in an intellectually honest way and execute quickly - what they call "speed of light" at Nvidia.

692,758 次观看 • 1 年前

Marc Andreesen on why building companies will become more expensive in the AI Era. There's a notion that AI will help cut down costs and increase efficiency. However, Marc presented a strong counterargument by referencing Jevon's paradox. Jevon's paradox: The Jevon's paradox occurs when technological progress increases the efficiency with which a resource is used (reducing the amount necessary for any one use), but the falling cost of use induces increases in demand enough that resource use is increased, rather than reduced. He gave some eg of Jevon's Paradox: ->Building roads will only lead to more cars & resulting in traffic. ->CGI in hollywood was developed to reduce the cost of film making but people's expectation increased, so cost involved in CGI also increased. ->Coal consumption increased in Industrial Revolution when the coal prices decreased. He says,The paradox here making cost of a given piece of software would be reduced, but the massive surge of demand for more powerful softwares will actually increase the cost of building a software company. Customers will start seeking for more & more powerful features.

Marc Andreesen on why building companies will become more expensive in the AI Era. There's a notion that AI will help cut down costs and increase efficiency. However, Marc presented a strong counterargument by referencing Jevon's paradox. Jevon's paradox: The Jevon's paradox occurs when technological progress increases the efficiency with which a resource is used (reducing the amount necessary for any one use), but the falling cost of use induces increases in demand enough that resource use is increased, rather than reduced. He gave some eg of Jevon's Paradox: ->Building roads will only lead to more cars & resulting in traffic. ->CGI in hollywood was developed to reduce the cost of film making but people's expectation increased, so cost involved in CGI also increased. ->Coal consumption increased in Industrial Revolution when the coal prices decreased. He says,The paradox here making cost of a given piece of software would be reduced, but the massive surge of demand for more powerful softwares will actually increase the cost of building a software company. Customers will start seeking for more & more powerful features.

810,559 次观看 • 2 年前

In DoorDash's early hiring process, Tony Xu would give applicants $20 and 20 mins to ask him anything, challenging them to figure out how to acquire 100 customers in 8 hrs and actually execute it. He'll also put a plane ticket by side incase if they want to quit the interview. "I understand that this may not be what you signed up for but this is what it's going to feel like on most days, where you have no idea what to do then you're just going to have to do something about it "

In DoorDash's early hiring process, Tony Xu would give applicants $20 and 20 mins to ask him anything, challenging them to figure out how to acquire 100 customers in 8 hrs and actually execute it. He'll also put a plane ticket by side incase if they want to quit the interview. "I understand that this may not be what you signed up for but this is what it's going to feel like on most days, where you have no idea what to do then you're just going to have to do something about it "

646,532 次观看 • 1 年前

Aravind Srinivas's hypothesis on how advertising works with AI agents is quite interesting. Aravind explains, Instead of showing ads to humans directly, advertisements would be targeted at AI agents that work on users' behalf. Users never see ads, they simply tell their AI agents what they need for eg if he gave, "I'm going to go to Rambagh for two nights, plan my schedule, do my bookings" In the backend, Multiple vendors compete for the agent's attention & companies bid for the agent's consideration. So everything happens at the AI level. Agents can also use more compute power to analyse offer to find out advertising attempts & make decisions based on user preferences. Also users can instruct their agents to ignore certain brands , this will remain confidential to between the agent & the user. This creates new revenue streams for AI companies & gives a better user experience.

Aravind Srinivas's hypothesis on how advertising works with AI agents is quite interesting. Aravind explains, Instead of showing ads to humans directly, advertisements would be targeted at AI agents that work on users' behalf. Users never see ads, they simply tell their AI agents what they need for eg if he gave, "I'm going to go to Rambagh for two nights, plan my schedule, do my bookings" In the backend, Multiple vendors compete for the agent's attention & companies bid for the agent's consideration. So everything happens at the AI level. Agents can also use more compute power to analyse offer to find out advertising attempts & make decisions based on user preferences. Also users can instruct their agents to ignore certain brands , this will remain confidential to between the agent & the user. This creates new revenue streams for AI companies & gives a better user experience.

590,837 次观看 • 1 年前

This story about how Zuck pushed Aditya Agarwal to his extreme & made him build the search engine in the early days is awesome! Aditya was one of the early engg in Facebook & in week 2 of joining he was still figuring out what to do,none were giving any directions. Mark walked upto him & asked him to build a search engine -just him he didn't mean a team. Because people want to search for others through the search engine in Facebook & this is crucial. Aditya was pretty skeptical about himself as he didn't know how to build one. He suggested hiring someone from Google or Yahoo to build this out, Mark was like: "Dude if I can build Facebook you can build a damn search engine" And he actually ended up doing it! Still now he shares this lesson from this defining moment of his life to every founders - Anything could be done if you'd put your mind into it.

This story about how Zuck pushed Aditya Agarwal to his extreme & made him build the search engine in the early days is awesome! Aditya was one of the early engg in Facebook & in week 2 of joining he was still figuring out what to do,none were giving any directions. Mark walked upto him & asked him to build a search engine -just him he didn't mean a team. Because people want to search for others through the search engine in Facebook & this is crucial. Aditya was pretty skeptical about himself as he didn't know how to build one. He suggested hiring someone from Google or Yahoo to build this out, Mark was like: "Dude if I can build Facebook you can build a damn search engine" And he actually ended up doing it! Still now he shares this lesson from this defining moment of his life to every founders - Anything could be done if you'd put your mind into it.

630,651 次观看 • 2 年前

Windsurf CEO explains why traditional startup moats are mostly silly. Varun said even with 50-100 of the best MIT engineers, that's still just "hundreds of engineering years" in your product. Someone else can build something similar in the same space. He used Nvidia as an example. People think CUDA is their real moat, but that's not accurate. Large companies like Google, OpenAI, and Anthropic spend tens of billions on chips. If CUDA didn't exist, they'd find a way to write assembly code and make GPUs work. People use CUDA because Nvidia made it really capable at low floating-point math, great interconnect, and fast computers. Not because they're locked in. Every year Nvidia has pressure - if they don't make hardware, interconnect, and memory bandwidth much faster, their profit margins shrink and AMD will compete. For startups, the only moat is speed. Learning where the dead bodies are. Understanding what doesn't work and building compounding advantages through that knowledge. Speed lets you learn from the market faster, which means you're first to the next idea too.

Windsurf CEO explains why traditional startup moats are mostly silly. Varun said even with 50-100 of the best MIT engineers, that's still just "hundreds of engineering years" in your product. Someone else can build something similar in the same space. He used Nvidia as an example. People think CUDA is their real moat, but that's not accurate. Large companies like Google, OpenAI, and Anthropic spend tens of billions on chips. If CUDA didn't exist, they'd find a way to write assembly code and make GPUs work. People use CUDA because Nvidia made it really capable at low floating-point math, great interconnect, and fast computers. Not because they're locked in. Every year Nvidia has pressure - if they don't make hardware, interconnect, and memory bandwidth much faster, their profit margins shrink and AMD will compete. For startups, the only moat is speed. Learning where the dead bodies are. Understanding what doesn't work and building compounding advantages through that knowledge. Speed lets you learn from the market faster, which means you're first to the next idea too.

334,732 次观看 • 1 年前

Bob McGrew (Head of Research OpenAI) explains why proprietary data no longer provides companies with a competitive advantage in the AI era. Finance companies once believed their years of accumulated data would give them an edge. They planned to train specialized models on top of GPT or Llama using their exclusive information. The results shocked them. Their industry-specific models performed worse than the next generation of general purpose models. The ability to synthesize new information proved more valuable than memorizing old data. McGrew introduces the concept of "embodied labor" - the human work behind data collection. Companies spent years having employees call customers, analyze case studies, and gather information through manual processes. This accumulated knowledge required massive time and money to build. It represented thousands of hours of human effort that companies thought couldn't be replicated by competitors. But AI changes everything. Instead of years of customer calls, AI can conduct comprehensive surveys instantly. Rather than manual case analysis, AI processes thousands of examples in hours. The core insight is that value wasn't in the data itself but in the labor required to collect it. Since AI makes that labor essentially free, the advantage disappears. Companies can no longer rely on their proprietary data as a protective moat. Any competitor can use AI to replicate years of data gathering almost instantly.

Bob McGrew (Head of Research OpenAI) explains why proprietary data no longer provides companies with a competitive advantage in the AI era. Finance companies once believed their years of accumulated data would give them an edge. They planned to train specialized models on top of GPT or Llama using their exclusive information. The results shocked them. Their industry-specific models performed worse than the next generation of general purpose models. The ability to synthesize new information proved more valuable than memorizing old data. McGrew introduces the concept of "embodied labor" - the human work behind data collection. Companies spent years having employees call customers, analyze case studies, and gather information through manual processes. This accumulated knowledge required massive time and money to build. It represented thousands of hours of human effort that companies thought couldn't be replicated by competitors. But AI changes everything. Instead of years of customer calls, AI can conduct comprehensive surveys instantly. Rather than manual case analysis, AI processes thousands of examples in hours. The core insight is that value wasn't in the data itself but in the labor required to collect it. Since AI makes that labor essentially free, the advantage disappears. Companies can no longer rely on their proprietary data as a protective moat. Any competitor can use AI to replicate years of data gathering almost instantly.

240,743 次观看 • 1 年前

Varun Mohan (Windsurf CEO) on hiring —don’t hire unless someone on the team raises their hand saying, “I’m drowning.” They don’t romanticize being a small team. The goal isn’t lean for lean’s sake. It’s to be the smallest team possible that can still build toward their ambition. If they’re building something as ambitious as rethinking how software is made, they’ll need people. But they’ll only hire once the current team is stretched thin. Varun compares it to a dehydrated body. Each hire is a drop of water. You only drink when you’re thirsty. That forces clarity on what’s truly important. It also avoids the worst problem with premature hiring: people inventing projects to stay relevant. Not because they’re bad—just because they weren’t really needed in the first place.

Varun Mohan (Windsurf CEO) on hiring —don’t hire unless someone on the team raises their hand saying, “I’m drowning.” They don’t romanticize being a small team. The goal isn’t lean for lean’s sake. It’s to be the smallest team possible that can still build toward their ambition. If they’re building something as ambitious as rethinking how software is made, they’ll need people. But they’ll only hire once the current team is stretched thin. Varun compares it to a dehydrated body. Each hire is a drop of water. You only drink when you’re thirsty. That forces clarity on what’s truly important. It also avoids the worst problem with premature hiring: people inventing projects to stay relevant. Not because they’re bad—just because they weren’t really needed in the first place.

172,403 次观看 • 1 年前

Andrej Karpathy explains why human-AI collaboration often fails: we've got the workflow backwards and the bottleneck wrong. He points out that when working with AI, there's a clear pattern. The AI generates solutions quickly while humans verify the output. The goal is making this loop as fast as possible to get real work done. The first way to speed this up is through better verification tools. GUIs are crucial because they tap into our brain's visual processing power. Reading through walls of text is slow and painful, but visual interfaces create a highway to understanding. The second approach is keeping AI constrained. Karpathy warns that people are getting too excited about autonomous agents. An AI that instantly generates 10,000 lines of code isn't helpful when a human still needs hours to verify it's bug-free and secure. The fundamental problem is that even instant AI generation becomes useless if humans can't verify fast enough. The human becomes the bottleneck, having to check for bugs, correct implementation, and security issues in massive outputs. His solution is simple: constrain AI output to maintain manageable verification loops. Don't let AI run wild with massive changes. Keep it on a leash so humans can actually review and approve the work effectively.

Andrej Karpathy explains why human-AI collaboration often fails: we've got the workflow backwards and the bottleneck wrong. He points out that when working with AI, there's a clear pattern. The AI generates solutions quickly while humans verify the output. The goal is making this loop as fast as possible to get real work done. The first way to speed this up is through better verification tools. GUIs are crucial because they tap into our brain's visual processing power. Reading through walls of text is slow and painful, but visual interfaces create a highway to understanding. The second approach is keeping AI constrained. Karpathy warns that people are getting too excited about autonomous agents. An AI that instantly generates 10,000 lines of code isn't helpful when a human still needs hours to verify it's bug-free and secure. The fundamental problem is that even instant AI generation becomes useless if humans can't verify fast enough. The human becomes the bottleneck, having to check for bugs, correct implementation, and security issues in massive outputs. His solution is simple: constrain AI output to maintain manageable verification loops. Don't let AI run wild with massive changes. Keep it on a leash so humans can actually review and approve the work effectively.

111,485 次观看 • 1 年前

"Elon committed the enormous sin of creating a new American manufacturing dynamo of a company." Tesla workers are among the highest-paid manufacturing workers globally. Many Tesla workers have reportedly become millionaires through stock compensation. There's political opposition to Musk despite Tesla creating the type of manufacturing jobs politicians claim to want. Building Tesla is really no joke, one of the hardest things for sure!

"Elon committed the enormous sin of creating a new American manufacturing dynamo of a company." Tesla workers are among the highest-paid manufacturing workers globally. Many Tesla workers have reportedly become millionaires through stock compensation. There's political opposition to Musk despite Tesla creating the type of manufacturing jobs politicians claim to want. Building Tesla is really no joke, one of the hardest things for sure!

151,548 次观看 • 1 年前

.Amjad Masad suggests SaaS founders to go against Silicon Valley's dogma w/ "focus" and build Chinese style tech companies - go broad, build platforms not point solutions. The SaaS market is getting brutally reshaped by AI. Every day Amjad sees people using Replit to replace $15k-100k software products. Vertical SaaS companies are on death watch. If your product solves one specific problem, it's already possible to clone it with today's AI. Tomorrow's tools will make it even easier. Horizontal platforms with strong ecosystems - think Rippling as your "system of record" or Salesforce with its massive developer network are more likely to survive. These aren't getting replaced by a weekend project. He predicts that generative capabilities and larger pieces of software will become more important.

.Amjad Masad suggests SaaS founders to go against Silicon Valley's dogma w/ "focus" and build Chinese style tech companies - go broad, build platforms not point solutions. The SaaS market is getting brutally reshaped by AI. Every day Amjad sees people using Replit to replace $15k-100k software products. Vertical SaaS companies are on death watch. If your product solves one specific problem, it's already possible to clone it with today's AI. Tomorrow's tools will make it even easier. Horizontal platforms with strong ecosystems - think Rippling as your "system of record" or Salesforce with its massive developer network are more likely to survive. These aren't getting replaced by a weekend project. He predicts that generative capabilities and larger pieces of software will become more important.

116,658 次观看 • 1 年前

Peter Thiel & Max Levchin had to pitch 150 investors to get their 1st check for Paypal. The story of how they raised money for Paypal

Peter Thiel & Max Levchin had to pitch 150 investors to get their 1st check for Paypal. The story of how they raised money for Paypal

102,877 次观看 • 2 年前

Sam Altman on how startups can survive GPT's advancements & build a sustainable business. The framework he found which was working is either building a business which bets against the AI model capabilities or a business which relies on the next gen AI models. He suggests not to build AI business but rather a business which has AI as a technology. Also gave eg w/ the early days of Appstore. People were building like flashlight apps which got replaced after iOS upgrade. Also included how Uber made a sustainable business when smartphones got better, as they used smartphones as technology which enabled their business majorly. In GPT's context it would be people doing a lot of work to make one use case which's just beyond the capability of GPT4. The next model will surely replace it.But building products to make it work across the board generically instead of focusing on one use case will get more benefitted w/ GPT5 capabilities. It would be like the "rising tide lifts all your boats" effect.

Sam Altman on how startups can survive GPT's advancements & build a sustainable business. The framework he found which was working is either building a business which bets against the AI model capabilities or a business which relies on the next gen AI models. He suggests not to build AI business but rather a business which has AI as a technology. Also gave eg w/ the early days of Appstore. People were building like flashlight apps which got replaced after iOS upgrade. Also included how Uber made a sustainable business when smartphones got better, as they used smartphones as technology which enabled their business majorly. In GPT's context it would be people doing a lot of work to make one use case which's just beyond the capability of GPT4. The next model will surely replace it.But building products to make it work across the board generically instead of focusing on one use case will get more benefitted w/ GPT5 capabilities. It would be like the "rising tide lifts all your boats" effect.

60,363 次观看 • 2 年前

Even if AI will write 90% of code. But it won’t replace engineers. Windsurf’s founder explains that even if AI generates most of the code, engineers still spend time reviewing it, debugging issues, designing systems, testing, deploying, and making architectural decisions. Those parts don’t disappear. He brings up Amdahl’s Law to make the point: speeding up one part of the workflow doesn’t dramatically change the total time unless everything else speeds up too. For example, if writing code takes 30 out of 100 total time units, and AI brings it down to 3, the total time still only drops to 73. That’s just a 27% improvement. More productivity doesn’t mean fewer engineers. It just increases the ROI of building tech. And when ROI goes up, companies are more likely to build—and hire more people to do it. Take JPMorgan Chase. They have 50,000+ engineers and a $17B software budget. If each engineer can ship more software, that doesn’t reduce headcount. It raises the cost of not building. Companies like Windsurf and Anthropic are hiring more engineers—not less—despite being at the edge of AI code generation.

Even if AI will write 90% of code. But it won’t replace engineers. Windsurf’s founder explains that even if AI generates most of the code, engineers still spend time reviewing it, debugging issues, designing systems, testing, deploying, and making architectural decisions. Those parts don’t disappear. He brings up Amdahl’s Law to make the point: speeding up one part of the workflow doesn’t dramatically change the total time unless everything else speeds up too. For example, if writing code takes 30 out of 100 total time units, and AI brings it down to 3, the total time still only drops to 73. That’s just a 27% improvement. More productivity doesn’t mean fewer engineers. It just increases the ROI of building tech. And when ROI goes up, companies are more likely to build—and hire more people to do it. Take JPMorgan Chase. They have 50,000+ engineers and a $17B software budget. If each engineer can ship more software, that doesn’t reduce headcount. It raises the cost of not building. Companies like Windsurf and Anthropic are hiring more engineers—not less—despite being at the edge of AI code generation.

27,268 次观看 • 1 年前

This story from Jessica Livingston about Sam Altman when he was 19 really talks about his personality. When PG was talking to Sam about interviewing for YC's first batch Sam emailed - "I'm pitching at a Stanford pitch contest so I can only interview on Saturday and not Sunday" PG replied back: "You know Sam you're only a freshman why don't you come next yr, this isn't your only chance" Sam replied with one line: "I'm a Sophomore & I'm coming" And he really did, then the interview for his first company loops was a no-brainer, they were really impressed by him.

This story from Jessica Livingston about Sam Altman when he was 19 really talks about his personality. When PG was talking to Sam about interviewing for YC's first batch Sam emailed - "I'm pitching at a Stanford pitch contest so I can only interview on Saturday and not Sunday" PG replied back: "You know Sam you're only a freshman why don't you come next yr, this isn't your only chance" Sam replied with one line: "I'm a Sophomore & I'm coming" And he really did, then the interview for his first company loops was a no-brainer, they were really impressed by him.

34,571 次观看 • 2 年前