Lessons from Investment Gurus

The Most Important Thing

 Successful investing requires thoughtful attention to many separate aspects, all at the same time. 
Omit any one and the result is likely to be less than satisfactory.

The idea of the most important things—each is a brick in a solid wall, and none is dispensable. 

It’s high time we started a generative AI mastermind thread:

What are some of the coolest use cases you’ve come across?

What tools are you actually using to make your work easier?

For those of us who invest in startups: what areas of opportunity are you focused on?

It’s high time we started a generative AI mastermind thread:  What are some of the coolest use cases you’ve come across?  What tools are you actually using to make your work easier?  For those of us who invest in startups: what areas of opportunity are you focused on?

Alex

6 months ago it looked like AI / LLMs were going to bring a much needed revival to the venture startup ecosystem after a few tough years. 

With companies like Jasper starting to slow down, it’s looking like this may not be the case. 

Right now there are 2 clear winners, a handful of losers, and a small group of moonshots that seem promising. 

Let’s start with the losers. 

Companies like Jasper and the VCs that back them are the biggest losers right now. Jasper raised >$100M at a 10-figure valuation for what is essentially a generic, thin wrapper around OpenAI. Their UX and brand are good, but not great, and competition from companies building differentiated products specifically for high-value niches are making it very hard to grow with such a generic product. I’m not sure how this pans out but VC’s will likely lose their money. 

The other category of losers are the VC-backed teams building at the application layer that raised $250K-25M in Dec - March on the back of the chatbot craze with the expectation that they would be able to sell to later-stage and enterprise companies. These startups typically have products that are more focused than something very generic like Jasper, but still don't have a real technology moat; the products are easy to copy. 

Executives at enterprise companies are excited about AI, and have been vocal about this from the beginning. This led a lot of founders and VC's to believe these companies would make good first customers. What the startups building for these companies failed to realize is just how aligned and savvy executives and the engineers they manage would be at quickly getting AI into production using open-source tools. An engineering leader would rather spin up their own LangChain (https://langchain.com)  and Chroma (https://www.trychroma.com) infrastructure for free and build tech themselves than buy something from a new, unproven startup (and maybe pick up a promotion along the way). 

In short, large companies are opting to write their own AI success stories rather than being a part of the growth metrics a new AI startup needs to raise their next round.  (This is part of an ongoing shift in the way technology is adopted)

This brings us to our first group of winners  —  established companies and market incumbents. Most of them had little trouble adding AI into their products or hacking together some sort of "chat-your-docs" application internally for employee use. This came as a surprise to many. Most of these companies seemed to be asleep at the wheel for years. They somehow woke up and have been able to successfully navigate the LLM craze with ample dexterity. 

There are two causes for this:
1. Getting AI right is a life or death proposition for many of these companies and their executives; failure here would mean a slow death over the next several years. They can't risk putting their future in the hands of a new startup that could fail and would rather lead projects internally to make absolutely sure things go as intended.
2. There is a certain amount of kick-ass wafting through halls of the C-Suite right now. Ambitious projects are being green-lit and supported in ways they weren't a few years ago. Hate to say it, but I think we owe this in part to Elon reminding us of what is possible when a small group of smart people are highly motivated to get things done. Reduce red-tape, increase personal responsibility, and watch the magic happen.

Our second group of winners live on the opposite side of this spectrum; indie devs and solopreneurs. These small, often one-man outfits do not raise outside capital or build big teams. They're advantage is their small size and ability to move very quickly with low overhead. They build niche products for niche markets, which they often dominate. The goal is build a saas product (or multiple) that generates ~$10k/mo in relatively passive income. This is sometimes called "mirco-saas."

This is the biggest group of winners right now. Unconstrained by the need for a $1B+ exit or the goal of $100MM ARR, they build and launch products in rapid-fire fashion, iterating until PMF and cashflow, and moving on to the next. 

LLMs and text-to-image models a la Stable Diffusion have been a boon for these entrepreneurs, and I personally know of dozens of successful (keeping in mind their definition of successful) apps that were started less than 6 months ago. The lifestyle and freedom these endeavors afford to those that perform well is also quite enticing. 

I think we will continue to see the number of successful micro-saas AI apps grow in the next 12 months. This could possibly become one of the biggest cohorts creating real value with this technology. 

The last group I want to talk about are the AI Moonshots — companies that are fundamentally re-imagining an entire industry from the ground up. Generally, these companies are VC-backed and building products that have the potential to redefine how a small group of highly-skilled humans interact with and are assisted by technology. It's too early to tell if they'll be successful or not; early prototypes have been compelling. This is certainly the most exciting segment to watch. 

A few companies I would put in this group are:
1. https://cursor.so  - an AI-first code editor that could very well change how software is written.
2.  https://harvey.ai - AI for legal practices
3.   https://runwayml.com - an AI-powered video editor

This is an incomplete list, but overall I think the Moonshot category needs to grow massively if we're going to see the #Artificial Intelligence-powered future we've been hoping for.

If you're a founder in the $250K-25M raised category and are having a hard time finding PMF for your chatbot or LLMOps company, it may be time to consider pivoting to something more ambitious.

Lets recap:
1. VC-backed companies are having a hard time. The more money a company raised, the more pain they're feeling.
2. Incumbents and market leaders are quickly become adept at deploying cutting-edge AI using internal teams and open-source, off-the-shelf technology, cutting out what seemed to be good opportunities for VC-backed startups.
3. Indie devs are building small, cash-flowing businesses by quickly shipping niche AI-powered products in niche markets.
4. A small number of promising Moonshot companies with unproven technology hold the most potential for VC-sized returns.

It's still early. This landscape will continue to change as new foundational models are released and toolchains improve. I'm sure you can find counter examples to everything written about here. Put them in the comments for others to see.

h/t Sam Hogan.

Artificial Intelligence

6 months ago it looked like AI / LLMs were going to bring a much needed revival to the venture startup ecosystem after a few tough years.   With companies like Jasper starting to slow down, it’s looking like this may not be the case.   Right now there are 2 clear winners, a handful of losers, and a small group of moonshots that seem promising.   Let’s start with the losers.   Companies like Jasper and the VCs that back them are the biggest losers right now. Jasper raised >$100M at a 10-figure valuation for what is essentially a generic, thin wrapper around OpenAI. Their UX and brand are good, but not great, and competition from companies building differentiated products specifically for high-value niches are making it very hard to grow with such a generic product. I’m not sure how this pans out but VC’s will likely lose their money.   The other category of losers are the VC-backed teams building at the application layer that raised $250K-25M in Dec - March on the back of the chatbot craze with the expectation that they would be able to sell to later-stage and enterprise companies. These startups typically have products that are more focused than something very generic like Jasper, but still don't have a real technology moat; the products are easy to copy.   Executives at enterprise companies are excited about AI, and have been vocal about this from the beginning. This led a lot of founders and VC's to believe these companies would make good first customers. What the startups building for these companies failed to realize is just how aligned and savvy executives and the engineers they manage would be at quickly getting AI into production using open-source tools. An engineering leader would rather spin up their own LangChain (https://langchain.com)  and Chroma (https://www.trychroma.com) infrastructure for free and build tech themselves than buy something from a new, unproven startup (and maybe pick up a promotion along the way).   In short, large companies are opting to write their own AI success stories rather than being a part of the growth metrics a new AI startup needs to raise their next round.  (This is part of an ongoing shift in the way technology is adopted)  This brings us to our first group of winners  —  established companies and market incumbents. Most of them had little trouble adding AI into their products or hacking together some sort of "chat-your-docs" application internally for employee use. This came as a surprise to many. Most of these companies seemed to be asleep at the wheel for years. They somehow woke up and have been able to successfully navigate the LLM craze with ample dexterity.   There are two causes for this: 1. Getting AI right is a life or death proposition for many of these companies and their executives; failure here would mean a slow death over the next several years. They can't risk putting their future in the hands of a new startup that could fail and would rather lead projects internally to make absolutely sure things go as intended. 2. There is a certain amount of kick-ass wafting through halls of the C-Suite right now. Ambitious projects are being green-lit and supported in ways they weren't a few years ago. Hate to say it, but I think we owe this in part to Elon reminding us of what is possible when a small group of smart people are highly motivated to get things done. Reduce red-tape, increase personal responsibility, and watch the magic happen.  Our second group of winners live on the opposite side of this spectrum; indie devs and solopreneurs. These small, often one-man outfits do not raise outside capital or build big teams. They're advantage is their small size and ability to move very quickly with low overhead. They build niche products for niche markets, which they often dominate. The goal is build a saas product (or multiple) that generates ~$10k/mo in relatively passive income. This is sometimes called "mirco-saas."  This is the biggest group of winners right now. Unconstrained by the need for a $1B+ exit or the goal of $100MM ARR, they build and launch products in rapid-fire fashion, iterating until PMF and cashflow, and moving on to the next.   LLMs and text-to-image models a la Stable Diffusion have been a boon for these entrepreneurs, and I personally know of dozens of successful (keeping in mind their definition of successful) apps that were started less than 6 months ago. The lifestyle and freedom these endeavors afford to those that perform well is also quite enticing.   I think we will continue to see the number of successful micro-saas AI apps grow in the next 12 months. This could possibly become one of the biggest cohorts creating real value with this technology.   The last group I want to talk about are the AI Moonshots — companies that are fundamentally re-imagining an entire industry from the ground up. Generally, these companies are VC-backed and building products that have the potential to redefine how a small group of highly-skilled humans interact with and are assisted by technology. It's too early to tell if they'll be successful or not; early prototypes have been compelling. This is certainly the most exciting segment to watch.   A few companies I would put in this group are: 1. https://cursor.so  - an AI-first code editor that could very well change how software is written. 2.  https://harvey.ai - AI for legal practices 3.   https://runwayml.com - an AI-powered video editor  This is an incomplete list, but overall I think the Moonshot category needs to grow massively if we're going to see the #Artificial Intelligence-powered future we've been hoping for.  If you're a founder in the $250K-25M raised category and are having a hard time finding PMF for your chatbot or LLMOps company, it may be time to consider pivoting to something more ambitious.  Lets recap: 1. VC-backed companies are having a hard time. The more money a company raised, the more pain they're feeling. 2. Incumbents and market leaders are quickly become adept at deploying cutting-edge AI using internal teams and open-source, off-the-shelf technology, cutting out what seemed to be good opportunities for VC-backed startups. 3. Indie devs are building small, cash-flowing businesses by quickly shipping niche AI-powered products in niche markets. 4. A small number of promising Moonshot companies with unproven technology hold the most potential for VC-sized returns.  It's still early. This landscape will continue to change as new foundational models are released and toolchains improve. I'm sure you can find counter examples to everything written about here. Put them in the comments for others to see.  h/t Sam Hogan.

Cursor is the IDE of the future, built for pair-programming with Powerful AI.

Cursor - The AI-first Code Editor

Illegitimi non carborundum

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