Chamath Palihapitiya, the “King of SPACs,” lost his investors more than $12B with his 6 SPAC IPOs. Today, Clover and Akili have 0 enterprise value. Virgin Galactic’s is barely hovering at ~$100M.
If you invested $100 into each of Chamath’s SPACs at the peak of the market in Dec 2021, you’d have lost a whopping 73% of your investment. That’s worse than the S&P 500 (-9%), all SPACS (-32%), bitcoin (-44%), and the memestock GameStop (-54%).
So much for the Warren Buffet of the Reddit age. Amazingly, the real Buffet generated a positive return of 22% during the same period.
The poor performance of SPACs — and of Chamath — is a fantastic demonstration of the destructive power of poorly constructed incentives.
—
SPACs were all the rage during the stock mania of 2020 and 2021. Proponents of SPACs argue that they “democratize” access to private unicorns that generally delay going public because of the laborious IPO process.
SPACs provide a way for any private company to go public quickly. SPAC sponsors first IPO a shell company, typically raising hundreds of millions of proceeds. They then hunt for unicorns to “buy” and take public, merging the shell company with the private company.
For doing all this work, the sponsors are compensated with SPAC founder shares worth roughly 20% of the initial capital raised (eg $40M on $200M raised).
Therein lies in the problem. Sponsors are compensated handsomely regardless of these companies’ long-term performance. They just had to make the target companies sound appealing enough to attract enough investors for enough time to sell their founder shares.
—
I gotta give it to him. Chamath really is a great poker player. He cashed in his chips and profited off these deals to the tune of $750M. But those who believed and went “all-in” weren’t so lucky.
해시태그#Chamath 해시태그#SPAC 해시태그#Investing 해시태그#Markets
If you invested $100 into each of Chamath’s SPACs at the peak of the market in Dec 2021, you’d have lost a whopping 73% of your investment. That’s worse than the S&P 500 (-9%), all SPACS (-32%), bitcoin (-44%), and the memestock GameStop (-54%).
So much for the Warren Buffet of the Reddit age. Amazingly, the real Buffet generated a positive return of 22% during the same period.
The poor performance of SPACs — and of Chamath — is a fantastic demonstration of the destructive power of poorly constructed incentives.
—
SPACs were all the rage during the stock mania of 2020 and 2021. Proponents of SPACs argue that they “democratize” access to private unicorns that generally delay going public because of the laborious IPO process.
SPACs provide a way for any private company to go public quickly. SPAC sponsors first IPO a shell company, typically raising hundreds of millions of proceeds. They then hunt for unicorns to “buy” and take public, merging the shell company with the private company.
For doing all this work, the sponsors are compensated with SPAC founder shares worth roughly 20% of the initial capital raised (eg $40M on $200M raised).
Therein lies in the problem. Sponsors are compensated handsomely regardless of these companies’ long-term performance. They just had to make the target companies sound appealing enough to attract enough investors for enough time to sell their founder shares.
—
I gotta give it to him. Chamath really is a great poker player. He cashed in his chips and profited off these deals to the tune of $750M. But those who believed and went “all-in” weren’t so lucky.
해시태그#Chamath 해시태그#SPAC 해시태그#Investing 해시태그#Markets
제가 10.5일부터 SF에서 짧으면 1달 길면 Thanks Giving 까지 머물 예정인데요 😉️️️️️️ 주로 AI로 창업/투자하는 분들, AI Researcher/Engineer 그리고 AI가 아니더라도 재미있는 문제를 푸는 사람들과 교류할 생각입니다. 뉴욕과 남미 쪽도 다녀올 생각이에요!
SF에 있는 창업팀, 투자자, 빌더, 리서처 중에서 추천해주실만한 사람/회사가 있을까요?~ 직접 아시지 못해도 알려주시는 것만으로도 도움 될 것 같습니다. 콜드콜은 자신 있거든요 🫡️️ @startup_learner으로 DM 주세요.
소개해주셔서 만난 경우는 그 분과 대화하고 이야기했던 내용들을 상세히 공유드리도록 해볼게요!
SF에 있는 창업팀, 투자자, 빌더, 리서처 중에서 추천해주실만한 사람/회사가 있을까요?~ 직접 아시지 못해도 알려주시는 것만으로도 도움 될 것 같습니다. 콜드콜은 자신 있거든요 🫡️️ @startup_learner으로 DM 주세요.
소개해주셔서 만난 경우는 그 분과 대화하고 이야기했던 내용들을 상세히 공유드리도록 해볼게요!
👍4❤1
Continuous Learning_Startup & Investment pinned «제가 10.5일부터 SF에서 짧으면 1달 길면 Thanks Giving 까지 머물 예정인데요 😉️️️️️️ 주로 AI로 창업/투자하는 분들, AI Researcher/Engineer 그리고 AI가 아니더라도 재미있는 문제를 푸는 사람들과 교류할 생각입니다. 뉴욕과 남미 쪽도 다녀올 생각이에요! SF에 있는 창업팀, 투자자, 빌더, 리서처 중에서 추천해주실만한 사람/회사가 있을까요?~ 직접 아시지 못해도 알려주시는 것만으로도 도움 될 것 같습니다. 콜드콜은…»
https://www.sequoiacap.com/article/generative-ai-act-two/
This moment has been decades in the making. Six decades of Moore’s Law have given us the compute horsepower to process exaflops of data. Four decades of the internet (accelerated by COVID) have given us trillions of tokens’ worth of training data. Two decades of mobile and cloud computing have given every human a supercomputer in the palm of our hands. In other words, decades of technological progress have accumulated to create the necessary conditions for generative AI to take flight.
ChatGPT became the fastest-growing application with particularly strong product-market fit among students and developers; Midjourney became our collective creative muse and was reported to have reached hundreds of millions of dollars in revenue with a team of just eleven; and Character popularized AI entertainment and companionship and created the consumer “social” application we craved the most—with users spending two hours on average in-app.
Towards Act Two
These applications are different in nature than the first apps out of the gate. They tend to use foundation models as a piece of a more comprehensive solution rather than the entire solution. They introduce new editing interfaces, making the workflows stickier and the outputs better. They are often multi-modal.
The market is already beginning to transition from “Act 1” to “Act 2.” Examples of companies entering “Act 2” include Harvey, which is building custom LLMs for elite law firms; Glean, which is crawling and indexing our workspaces to make Generative AI more relevant at work; and Character and Ava, which are creating digital companions.
This reflects two important thrusts in the market: Generative AI’s evolution from technology hammer to actual use cases and value, and the increasingly multimodal nature of generative AI applications.
The moats are in the customers, not the data. the data that application companies generate does not create an insurmountable moat, and the next generations of foundation models may very well obliterate any data moats that startups generate. Rather, workflows and user networks seem to be creating more durable sources of competitive advantage.
In short, generative AI’s biggest problem is not finding use cases or demand or distribution, it is proving value. What are you going to use all this infrastructure to do? How is it going to change people’s lives?” The path to building enduring businesses will require fixing the retention problem and generating deep enough value for customers that they stick and become daily active users.
If you build model development stack products, you should be around with customers and the place maybe is Bay.
https://twitter.com/alexgraveley/status/1659276299091812353
This moment has been decades in the making. Six decades of Moore’s Law have given us the compute horsepower to process exaflops of data. Four decades of the internet (accelerated by COVID) have given us trillions of tokens’ worth of training data. Two decades of mobile and cloud computing have given every human a supercomputer in the palm of our hands. In other words, decades of technological progress have accumulated to create the necessary conditions for generative AI to take flight.
ChatGPT became the fastest-growing application with particularly strong product-market fit among students and developers; Midjourney became our collective creative muse and was reported to have reached hundreds of millions of dollars in revenue with a team of just eleven; and Character popularized AI entertainment and companionship and created the consumer “social” application we craved the most—with users spending two hours on average in-app.
Towards Act Two
These applications are different in nature than the first apps out of the gate. They tend to use foundation models as a piece of a more comprehensive solution rather than the entire solution. They introduce new editing interfaces, making the workflows stickier and the outputs better. They are often multi-modal.
The market is already beginning to transition from “Act 1” to “Act 2.” Examples of companies entering “Act 2” include Harvey, which is building custom LLMs for elite law firms; Glean, which is crawling and indexing our workspaces to make Generative AI more relevant at work; and Character and Ava, which are creating digital companions.
This reflects two important thrusts in the market: Generative AI’s evolution from technology hammer to actual use cases and value, and the increasingly multimodal nature of generative AI applications.
The moats are in the customers, not the data. the data that application companies generate does not create an insurmountable moat, and the next generations of foundation models may very well obliterate any data moats that startups generate. Rather, workflows and user networks seem to be creating more durable sources of competitive advantage.
In short, generative AI’s biggest problem is not finding use cases or demand or distribution, it is proving value. What are you going to use all this infrastructure to do? How is it going to change people’s lives?” The path to building enduring businesses will require fixing the retention problem and generating deep enough value for customers that they stick and become daily active users.
If you build model development stack products, you should be around with customers and the place maybe is Bay.
https://twitter.com/alexgraveley/status/1659276299091812353
Sequoia Capital
Generative AI’s Act Two
One year ago, we published a hypothesis that generative AI would become a profound platform shift in technology. Then came the firestorm.
empiricism is the key to progress
rationalism is the key to sounding smart
rationalism is the key to sounding smart
Continuous Learning_Startup & Investment
https://www.sequoiacap.com/article/generative-ai-act-two/ This moment has been decades in the making. Six decades of Moore’s Law have given us the compute horsepower to process exaflops of data. Four decades of the internet (accelerated by COVID) have given…
CPU/GPU는 우리에게 Computing Power로 데이터를 연산할 수 있는 능력을, 인터넷은 수많은 데이터를, 모바일과 클라우드는 모든 사람의 손에 쥘 수 있는 컴퓨터를 제공했기 때문에 Gen AI기반의 새로운 시대가 열릴 수 있었다. 마치 지금까지 기술의 발전이 AI로 새롭게 열릴 시대의 준비운동이였던 것처럼요.
Act 1은 기술이 재밌어서 사람들이 사용했다면 Act2는 진짜 문제를 해결해주는 시대다.
- 과거 인터넷도 처음엔 재미(Netscape)에서 시작해서 Amazon, Facebook, Google처럼 기능 중심의 서비스들이 나왔던 것 같아요.
- 스마트폰 카메라도 사진을 찍는 기술에서, Instagram이 사진을 공유할 수 있는 서비스를 만들면서 기존에 카메라가 못 풀던 어려움(외로움)을 해결?!해줬다고 생각합니다.
Moat은 데이터가 아니라 고객이다.
- 결국 데이터를 계속 만들어내는 주체는 고객이기 때문에 고객을 얼마나 많이 Lock-in(고객이 일상에서 그 제품을 쓰느냐)가 더 많은 데이터, 좋은 모델, 더 많은 고객을 락인하는 사이클을 만들 수 있는 것 같습니다.
모델 인프라 만들거라면 유저가 제일 많이 있는 Bay에 가서 고객을 만나면서 제품을 만드는 게 좋을 것 같다.
과거 스마트폰이 위치/카메라/인터넷 등 새롭게 가능했던 기능들을 조합해서 Uber, Airbnb, Instagram, Whatsapp이 나왔던 것처럼 AI라서 나오는 새로운 변화들을 잘 살린 프러덕이 고객에게 가치를 제공할 수 있다.
- Generative Interface: AI는 Input 을 넣으면 원하는 Output을 주는 수준을 넘어서 사람과 대화하고 교감하는 듯한 느낌을 제공할 수 있다.
- Editing Experiences: 컴퓨터가 원하는 결과물을 가져오지 못했을 때 이에 대해서 가이드를 주고 수정할 수 있다. 마치 팀원들과 협업하듯이?
- Agent system: 컴퓨터와 다르게 AI가 무슨 답을 줄지 예상하기 어렵다. 장점이 될 수도 단점이 될 수도 있는 부분.
- System-wide optimization: 기존에 Software는 판단하는 부분을 유저에게 모두 위임했다면, AI는 Workflow자체를 재설계할 수 있고 사람의 판단이 들어가는 부분을 AI가 자동화하되 사람에게 코칭받는 식으로 설계해볼 수 있다. 혹은 그런 코칭조차 AI들이 할 수 있는 구조도 나올 수 있다.
Research의 시대에서 Engineering과 제품의 시대로 넘어왔고 GPU가격은 여전히 비싸지만 그래도 몇년안에 해소가 된다면 좀 더 다양한 시도들이 가능해지지 않을까 하는 기대가 되네요.
Act 1은 기술이 재밌어서 사람들이 사용했다면 Act2는 진짜 문제를 해결해주는 시대다.
- 과거 인터넷도 처음엔 재미(Netscape)에서 시작해서 Amazon, Facebook, Google처럼 기능 중심의 서비스들이 나왔던 것 같아요.
- 스마트폰 카메라도 사진을 찍는 기술에서, Instagram이 사진을 공유할 수 있는 서비스를 만들면서 기존에 카메라가 못 풀던 어려움(외로움)을 해결?!해줬다고 생각합니다.
Moat은 데이터가 아니라 고객이다.
- 결국 데이터를 계속 만들어내는 주체는 고객이기 때문에 고객을 얼마나 많이 Lock-in(고객이 일상에서 그 제품을 쓰느냐)가 더 많은 데이터, 좋은 모델, 더 많은 고객을 락인하는 사이클을 만들 수 있는 것 같습니다.
모델 인프라 만들거라면 유저가 제일 많이 있는 Bay에 가서 고객을 만나면서 제품을 만드는 게 좋을 것 같다.
과거 스마트폰이 위치/카메라/인터넷 등 새롭게 가능했던 기능들을 조합해서 Uber, Airbnb, Instagram, Whatsapp이 나왔던 것처럼 AI라서 나오는 새로운 변화들을 잘 살린 프러덕이 고객에게 가치를 제공할 수 있다.
- Generative Interface: AI는 Input 을 넣으면 원하는 Output을 주는 수준을 넘어서 사람과 대화하고 교감하는 듯한 느낌을 제공할 수 있다.
- Editing Experiences: 컴퓨터가 원하는 결과물을 가져오지 못했을 때 이에 대해서 가이드를 주고 수정할 수 있다. 마치 팀원들과 협업하듯이?
- Agent system: 컴퓨터와 다르게 AI가 무슨 답을 줄지 예상하기 어렵다. 장점이 될 수도 단점이 될 수도 있는 부분.
- System-wide optimization: 기존에 Software는 판단하는 부분을 유저에게 모두 위임했다면, AI는 Workflow자체를 재설계할 수 있고 사람의 판단이 들어가는 부분을 AI가 자동화하되 사람에게 코칭받는 식으로 설계해볼 수 있다. 혹은 그런 코칭조차 AI들이 할 수 있는 구조도 나올 수 있다.
Research의 시대에서 Engineering과 제품의 시대로 넘어왔고 GPU가격은 여전히 비싸지만 그래도 몇년안에 해소가 된다면 좀 더 다양한 시도들이 가능해지지 않을까 하는 기대가 되네요.
👍2
https://www.sequoiacap.com/article/follow-the-gpus-perspective/
There is a large opportunity for the startup ecosystem to fill this hole. Our goal is to “follow the GPUs” and find the next generation of startups that leverage AI technology to create real end-customer value. We want to invest in these companies.
For startups, the takeaway is clear: As a community, we need to shift our thinking away from infrastructure and towards end-customer value. Happy customers are a fundamental requirement of every great business.
There is a large opportunity for the startup ecosystem to fill this hole. Our goal is to “follow the GPUs” and find the next generation of startups that leverage AI technology to create real end-customer value. We want to invest in these companies.
For startups, the takeaway is clear: As a community, we need to shift our thinking away from infrastructure and towards end-customer value. Happy customers are a fundamental requirement of every great business.
Sequoia Capital
AI’s $200B Question
GPU capacity is getting overbuilt. Long-term, this is good for startups. Short-term, things could get messy. Follow the GPUs to find out why.
My latest story: Other startups love Linear. Will bigger companies, too?
A maker of project software popular with other startups like Cohere and Ramp, Linear has raised $35M in Series B funding led by Accel. It's now valued at about $400M, sources told me for Forbes.
Linear's unusual in that it was already profitable for two years and had negative net burn -- meaning it has more money in the bank than it's raised. Until recently, it had just one salesperson, and it's spent just $36K on marketing over its four years of existence, CEO Karri Saarinen told me.
Founded by 3 Finns and fully-remote, Linear added former First Round Capital partner and Stripe and Notion veteran Cristina Cordova to lead go-to market earlier this year. A who's who of other tech leaders like Dylan Field, Patrick Collison, Dick Costolo and Claire Hughes Johnson are personal backers.
Now Linear is looking to push into bigger businesses, while expanding its tools to cover more points in the product life cycle -- something that excited Accel's Miles Clements. Customers like Job van der Voort of Remote and Anil Varanasi of Meter told me they're big fans... but the question is whether Linear can grow up without losing its magic.
해시태그#startups 해시태그#venturecapital 해시태그#funding 해시태그#fundraising 해시태그#VC 해시태그#developers 해시태그#developertools 해시태그#engineering 해시태그#software 해시태그#productdevelopment 해시태그#product 해시태그#projectmanagement 해시태그#productledgrowth 해시태그#PLG 해시태그#tech 해시태그#technology 해시태그#remotework 해시태그#DevOps
A maker of project software popular with other startups like Cohere and Ramp, Linear has raised $35M in Series B funding led by Accel. It's now valued at about $400M, sources told me for Forbes.
Linear's unusual in that it was already profitable for two years and had negative net burn -- meaning it has more money in the bank than it's raised. Until recently, it had just one salesperson, and it's spent just $36K on marketing over its four years of existence, CEO Karri Saarinen told me.
Founded by 3 Finns and fully-remote, Linear added former First Round Capital partner and Stripe and Notion veteran Cristina Cordova to lead go-to market earlier this year. A who's who of other tech leaders like Dylan Field, Patrick Collison, Dick Costolo and Claire Hughes Johnson are personal backers.
Now Linear is looking to push into bigger businesses, while expanding its tools to cover more points in the product life cycle -- something that excited Accel's Miles Clements. Customers like Job van der Voort of Remote and Anil Varanasi of Meter told me they're big fans... but the question is whether Linear can grow up without losing its magic.
해시태그#startups 해시태그#venturecapital 해시태그#funding 해시태그#fundraising 해시태그#VC 해시태그#developers 해시태그#developertools 해시태그#engineering 해시태그#software 해시태그#productdevelopment 해시태그#product 해시태그#projectmanagement 해시태그#productledgrowth 해시태그#PLG 해시태그#tech 해시태그#technology 해시태그#remotework 해시태그#DevOps
Good heuristic for creating SaaS startups: find a commonly used spreadsheet (eg cap tables) and turn it into a dashboard (eg Carta). Replace email attachments with workflows. Spreadsheets are the long tail of datasets that don’t have their own SaaS tool yet.
https://twitter.com/DavidSacks/status/1078755080478715904
https://twitter.com/DavidSacks/status/1078755080478715904
X (formerly Twitter)
David Sacks (@DavidSacks) on X
Good heuristic for creating SaaS startups: find a commonly used spreadsheet (eg cap tables) and turn it into a dashboard (eg Carta). Replace email attachments with workflows. Spreadsheets are the long tail of datasets that don’t have their own SaaS tool yet.
https://twitter.com/pitdesi/status/1705614393235386471?s=20
The #1 App right now is “Lapse” - a photo sharing Dispo-meets-Snapchat.
You will get a text message from a friend to download the app. It’s bc they require you text 5 friends to use the app. I felt dirty.
It got to the top of the App Store on a pyramid scheme.
Clientside SMS convert at around 30%. 5 invites x 0.3 = 1.5 K-Factor
The #1 App right now is “Lapse” - a photo sharing Dispo-meets-Snapchat.
You will get a text message from a friend to download the app. It’s bc they require you text 5 friends to use the app. I felt dirty.
It got to the top of the App Store on a pyramid scheme.
Clientside SMS convert at around 30%. 5 invites x 0.3 = 1.5 K-Factor
X (formerly Twitter)
Sheel Mohnot (@pitdesi) on X
Oops my tweet mentioned it’s the top app but showed a screenshot of the photo category. It’s actually the top app across all apps right now
https://hellometer.io/
According to operations research, for every 7 seconds of improvement in service speed, restaurants see about a 1% increase in top-line revenue. The average quick service restaurant generates about $1.9 million in revenue per year, so a 47-second improvement from Hellometer translates to approximately $130,000 in added revenue per location. Hellometer has been in business for three years and is a Y Combinator-backed company. It is currently under contract or letter of intent for over 400 locations worldwide, including Hardees, Dairy Queen, Dunkin', Subway, and Church's Chicken restaurants
According to operations research, for every 7 seconds of improvement in service speed, restaurants see about a 1% increase in top-line revenue. The average quick service restaurant generates about $1.9 million in revenue per year, so a 47-second improvement from Hellometer translates to approximately $130,000 in added revenue per location. Hellometer has been in business for three years and is a Y Combinator-backed company. It is currently under contract or letter of intent for over 400 locations worldwide, including Hardees, Dairy Queen, Dunkin', Subway, and Church's Chicken restaurants