Historically, enduring marketplaces have developed in parallel with new underlying infrastructure. As highlighted in our TechCrunch piece five years ago, the rise of Craigslist was made possible by the concurrent popularisation of email – and likewise eBay by the graphical browser; Uber by location services on smartphones; and Airbnb by the reputation systems of social networks.
LLMs and generative AI stand to be the next platform shift, enabling us to both interpret data and generate new content with unprecedented ease. We’ve recently shared our investing framework for generative AI applications, and now turn our attention to the next decade’s marketplaces. LLMs will enhance current offerings, allow for completely new exchange, and alter the economics of this business model.
The basic purpose of a traditional marketplace is to match supply and demand, and facilitate exchange. It accomplishes this by sourcing and onboarding suppliers, enabling product discovery, facilitating the transaction itself, and assisting with post-purchase support.
Supply sourcing: overcoming the cold start problem
Marketplaces add value by aggregating a fragmented supply base, and founders often tell us that sourcing supply can become an extremely time-consuming task, especially in the early days. This is the much-discussed “cold start problem”: it's hard to provide value to the demand side until you have supply, and vice versa. There are two distinct challenges:
How might LLMs eliminate this friction?
These new LLM-powered tools could reduce CAC during sourcing, and increase conversion during onboarding. The risk is that this may also lead to less sticky supply, as it might become easier to participate in multiple platforms over time.
Many marketplace suppliers are limited only by their own creativity, especially on maker- and creator-focused platforms like Etsy. LLMs might help these suppliers ideate and generate new product concepts, or aggregate and reimagine existing content.
LLMs are creating a new tension for certain marketplaces – like those for freelance work or digital goods (such as artwork and books). As these marketplaces have amassed extensive and detailed data on the goods they have sold over the years, LLMs might be used to directly generate some of these digital goods, personalising based on customers' purchase history and browsing behaviour.
This will raise some questions of IP and copyright, and will have knock-on effects on the creator economy: how does the role of the human supplier change?
Etsy's success was partly driven by a simple insight, that consumers love purchasing items crafted by 'real individuals' rather than faceless businesses. In this regard, we expect that LLMs will assist creators more than replace them outright. On the other hand, some services marketplaces might not even need a human supplier at all – indeed, the threat of generative AI was reflected in Fiverr’s declining stock price.
Listing is an obvious application for LLMs. The user’s goal is to list their products in the most engaging possible way, and generative AI products can assist with this:
These changes are particularly promising for consumer verticals that have a large volume of SKUs and frequent stock renewal – where the friction of listing is highest.
Navigating a traditional marketplace often involves tedious scrolling through many product pages. LLMs can augment recommendation engines, and enable tailored product discovery with a human, conversational touch.
Traditionally, boutique, luxury retailers sell in a much more personalised way than high-throughput online marketplaces. The former rely on clienteling — a personalised approach to cultivating long-term customer relationships — to boost conversion. This typically requires a large basket size to justify the associated human effort, so it has been limited to premium retailers.
LLMs can change this. The expensive and hard-to-find "agent-vendors" that marketplaces seldom or never utilised can now be readily available on their platforms. This transformation holds particular promise for high-volume verticals, like apparel, CPG, and even dating, where there is enough data to observe user preferences accurately, and the quality of product recommendations has an immediate impact on conversion.
In the past, successful marketplaces have refrained from offering extensive customer support services to protect their unit economics, especially in high volume/low commission models. LLMs can allow for better customer service at lower cost – voice and virtual assistants, can now provide quick and personalised real-time assistance for returns, disputes, and warranties. Marketplaces now have the opportunity to transform the post-purchase customer experience, boost NPS and increase retention.
When it comes to pricing, AI will have the greatest impact on complex purchases with a wide range of parameters and risks. Consider the purchase of a car: anyone who has ever bought or considered buying a vehicle online is aware of the complexity involved. The price one is willing to pay depends on multiple factors: both item-specific (e.g., colour, year, condition, options, mileage) and individual-specific (e.g., use, budget, financing, urgency of need).
Over time, one could imagine that buyers may be able to specify their preferences in natural language with an agent that infers the parameters and their weights. This bot would then run the negotiation with the supply side (or their own bots, which would rely on their own parameters such as available supply, minimum margin, and time-to-end-of-season) and bid on their behalf.
This could lead to more fine-grained dynamic pricing or staged service delivery in high-value transactions, optimising for the preferences of both sides of the marketplace.
Now, let’s delve into the types of platforms that were previously hard to build or operate. We split these into two categories: new verticals of goods and services, and new types of interaction.
LLMs can help us more efficiently aggregate supply, enhance listings and facilitate complex negotiations. This prompts us to ask: until now, which sectors of the economy have been either too expensive to transition online, hard to market, or complex to price?
Given low historic penetration rates, we focus on B2B segments, particularly those that involve high basket value and heterogeneous SKUs. For example, we can envision marketplaces for complex manufactured goods or multi-step human services, where traditionally they are bought offline. On the B2C side, we can project potential opportunities that share similar characteristics – such as more efficient travel services, or rare / unique items that have been difficult to price.
Eventually, LLMs could serve as a new “front end to the internet” – a new way to discover anything one might need (as Google has been for the last two decades). This leads us to contemplate whether a comparable transformation is on the horizon for online marketplaces, and whether a new abstraction layer will emerge – a "meta marketplace". This could look like a personalised agent which understands the user intimately, searches all corners of the internet to enable seamless purchases across many destinations.
If this layer emerges, the role of marketplaces will change substantially. Will they adopt defensive strategies to avoid involvement with these new aggregators, aiming to sustain a more direct rapport with their customers?
We’re not yet sure what the long-term impact of LLMs will be on the business model of marketplaces. Our hypotheses are:
As investors, we’re excited by three categories of startups:
Whether you are a marketplace founder leveraging LLMs – or have an altogether contrarian perspective – we would love to hear from you!