Throughout every new era of technology, investors have always been eager to back the so-called “picks and shovels” of the moment. Today, we see this in the valuations of NVIDIA and foundational models.
But, while others rush to back the enablers, someone still needs to find gold. I want to back founders who are willing to take the risk to build enduring, AI-enabled products that will change how people work and live.
I believe there’s tremendous value to be captured by product builders who can successfully put the power of AI into products that people love. As my partner Jerry Chen recently put forth, if we’re living in an age where foundation models make it possible for anyone to build an AI company, “the most strategic advantage [of applications] is that you can coexist with several systems of record and collect all the data that passes through your product.”
This is already happening with founders like Keith Peiris and Henri Liriani from Tome, or Cristóbal Valenzuela from Runway. They aren’t just using AI to enhance a product – they are using AI as the key unlock to drive their entire product development and business strategy.
Of course there are plenty of detractors who don’t believe startups have a chance at this layer – incumbents own the data and distribution, and access to LLMs is both commoditized and fraught with platform risk. There will likely be many casualties of companies where an API call to OpenAI isn’t sufficient to build lasting value.
In this post, I present my thesis for the next wave of AI-first products and outline a few ways founders might approach this opportunity.
As I see it, these are the three largest opportunities for founders to build AI-first companies:
- AI-first networks & marketplaces
- Re-defining enterprise software categories
- Co-pilot for services
AI-first Networks & Marketplaces
In the last wave of consumer software, social networks and marketplaces were the dominant business models that created trillions of dollars of market cap, with Meta alone valued at just under $800 billion. Greylock was lucky to back many of these, including Meta, LinkedIn, Roblox, Airbnb, Discord, Musical.ly (now TikTok), and Nextdoor.
As reflected by the valuations, these networks were assumed to be “unbreakable”.
But now, AI challenges many of our initial assumptions. This is creating a new arms race to build the next AI-first network.
We moved from networks that connect people to algorithms that connect people to content. Now, we’re moving to algorithms that replace people.
The progression, as I see it (and as Sam Lessin eloquently described):
- Pre-AI network → people connected with people and businesses
- AI-powered network → people posting & consuming content for/by the algorithm
- AI-only network → AI creating personalized content for each person
Beyond social networks, AI will impact a range of “bits only” networks including dating apps, gaming, labor marketplaces, and specialized skill marketplaces. Most incumbents will incorporate AI in some form, while others will overhaul their entire product to be AI-first. Most likely, incumbents will be slow to move, and entire categories will be up for grabs.
As founders evaluate AI-first marketplace opportunities, I’d consider two things:
- Marketplaces that generate unique data from participants
- Marketplaces that connect two sides, rather than replace one
To illustrate the point, let’s take two marketplaces that will be rebuilt using AI and compare them on these dimensions: a freelance logo design marketplace and a jobs marketplace.
You can imagine a freelance logo design marketplace, like parts of Fiverr, will be replaced with an algorithm. A user inputs a prompt, and after a few tries, gets their logo. In this case, the data the algorithm receives is fairly shallow (prompts and selection), and the supply side is entirely replaced by an algorithm.
Contrast this to an AI-first jobs marketplace. The optimal product would be an AI career coach for job seekers and an AI assistant for recruiters – two seemingly separate products, connected by the same algorithm. The coach could gather deep insight from a job seeker – far beyond what they would share on a resume or LinkedIn – and use this data to not just find the perfect match, but help them discover their most fulfilling career path. Combine this data with a strong understanding of a recruiter’s needs, and both the coach and assistant get better.
In this case, the product is designed to gather more nuanced data, and the AI is augmenting and connecting both supply (job seekers) and demand (recruiters) rather than replacing one.