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We're sharing a guest blog post from Colin Gardiner, covering how AI is impacting marketplaces, alongwith the rise of agent-led marketplaces. This was previously shared on Colin's Substack here and as a deep dive post in the community here.
Hi Everyone - I wanted to share my first long-form post for the year on the future of marketplaces! Please let me know your thoughts and feedback in the comments below.
We all remember the first time we used ChatGPT. The experience was magical, and the raw potential was eye-opening. In the last two years, ChatGPT and AI, generally, have changed the world as we know it. If software was eating the world, it was at a measured pace before, but now it is devouring it at a breakneck speed. Creative destruction is rampant: businesses are forced to evolve or perish, labor markets are in turmoil, and the future looks unclear.
What a way to end 2024 and begin 2025!
Far from being immune to AI disruption, marketplaces are uniquely positioned to benefit from the tailwinds AI agents unlock. The revolution will fundamentally change marketplaces as we know them.
Marketplaces are at the heart of economic activity. From the old bazaars to today’s global digital platforms, they have evolved to connect supply and demand more efficiently over time. Yet, despite all the advancements in modern marketplaces, a fundamental aspect remains unchanged: they rely on humans to drive decision-making. Buyers search, compare, and choose; sellers optimize, adjust, and compete. But what happens when AI steps in—not as a tool for efficiency but as the engine driving marketplace dynamics?
The answer is a massive unlocking of economic potential that could revolutionize marketplaces as we know them. Marketplaces can move from being passive products to being proactive ones.
Marketplaces are already harnessing AI, but the applications are becoming more robust with LLMs. AI-first search engines have been created to index and proactively use preferences to make suggestions. Today, they are prompt-based, but with the advent of agentic AI, marketplaces can harness AI agents to match supply and demand proactively.
I call these new types of marketplaces Agent-Led Marketplaces, or ALMs for short. They have the potential attributes:
The current wave of AI has primarily improved marketplaces' bottom-line efficiency. They use AI to cut costs, streamline operations, and enhance productivity.
Marketing, for example, has greatly benefited from AI. AI has improved efficiency through content creation, personalized campaigns, targeting, and optimized ad spending. These innovations are powerful but fundamentally incremental. They refine what we already do, making it faster and cheaper.
However, these systems rely on human intervention to set goals, analyze outputs, and make decisions. Even when AI contributes to decision-making, its role is bounded by predefined inputs and objectives, making AI a latent tool.
The real potential of AI in marketplaces lies not in optimizing processes but in reimagining them entirely. AI is the key to a new model in which the heavy lifting of aggregation, matching, and decision-making is no longer human-driven but agent-led.
One of AI’s greatest strengths is its ability to synthesize massive amounts of data to make complex recommendations. It doesn’t just filter options—it can deeply understand needs, predict preferences, and surface the best matches with unparalleled precision (when appropriately trained and not hallucinating). In traditional marketplaces, buyers must search, evaluate, and select from various options when they have time.
Imagine booking a vacation. Today, you might spend hours comparing flights, hotels, and activities on travel sites. Take Airbnb, for example. You must find and select the filters you care about and then submit them to find the rental that meets your needs. This process is very clunky and time-consuming.
What if you could have a natural language search that considered your preferences and filtered accordingly? What if the agent used your past bookings, reviews, and other data to improve those recommendations? What if the agent knew I took a trip every year at Spring Break with my kids and proactively made suggestions?
In an agent-led marketplace, this could be a reality; you could tell your AI agent, "I want a relaxing beach vacation with great food and some adventure options under $5,000." The agent would handle everything: finding the perfect destination, booking flights and accommodations, and even curating a personalized itinerary within your budget—keeping you updated and ensuring you’re in control.
Agentic AI can play a decisive role in actively redefining how marketplaces match supply and demand by shifting the paradigm from a faceted search to a prompt or chat-based experience that leads to goal-oriented agents adapting over time to drive marketplace outcomes proactively.
One company working on this today is PartyPlease, a marketplace for party planners, which switched from a standard grid search experience to a long-form chat that combines humans and agents. Jeremy Burton of Platform Venture Studio shared that this change improved booking conversion rates by 30%+, which is unsurprising, given that party planning is inherently customized and not a one-size-fits-all process.
I am not the first to highlight AI's potential to improve search. Many companies use some form of AI today to enhance their search algorithms. In his piece The AI-First Marketplace, Pete Flint of NFX aptly points out that AI can “Reimagine the Search Box.” As he points out, AI will shift from a push to a pull experience in search, making matching a more active than a passive experience.
Olivia Moore of a16z phrases it as “new search modalities” that lower the cognitive load of searching and the shoe leather costs for consumers.
“These changes will reduce scrolling fatigue and make it more likely for a search to convert into a transaction. If you’ve ever spent 20 minutes or more browsing DoorDash, you understand that closing the gap from “inspiration” to actually finding what you’re looking for is crucial to getting an order placed.”
Her post is excellent and highlights several worthwhile examples. Her example of Glaze, which uses natural language and AI to generate results based on style, vibe, etc., rather than standard facets like color, size, etc., illustrates the shift from push to pull experience.
Revolutionized search experiences only paint a portion of the bigger picture. The bigger picture is that agentic agents can act autonomously, allowing marketplaces to add value beyond user input into a search box. Marketplaces will benefit greatly when goal-oriented agents can pre-emptively and proactively perform the fundamental search and discovery tasks involved in making a transaction.
In agent-led marketplaces, you can imagine a system where:
Agent-led marketplaces have the inherent advantage of improving over time, but they also include softer and situational attributes that are difficult for faceted search to handle. Greater personalization and accuracy lead to trust and build long-term customer relationships, which is paramount for AI agents to be fully adopted by users.
For example:
Whitney Wolfe Herd of Bumble talks about this at length in her appearance at Bloomberg Tech earlier this year. The clip below is long (I start it at the right spot), but she talks at length about how innovation through AI will evolve the Bumble marketplace into a relationship platform. They will have AI agents that act as dating coaches and potentially even date for you.
A by-product of an inherently proactive system is that it becomes less transactional, which is paramount for dating. Goal-oriented AI opens the door to a more relational marketplace, where adaptive learning and value creation are built into every interaction. Building rapport and trust can allow customers to build a relationship with the marketplace, improving outcomes across conversion and retention.
The shift to agent-led marketplaces isn’t just about adding AI—it’s about transforming or building the marketplace's core to be AI-first. NFX does a great job articulating the spectrum of AI integration.
Today, the most economically significant marketplaces are between 1, 2, or 3, with an average near 1. For existing marketplaces to cross the AI chasm, they must rebuild to be AI-first, which will inherently advantage nimble and AI-savvy newcomers. For incumbents, my experience suggests they will find it challenging to make the expensive and risky bet to refactor and disrupt the way they do business entirely. Their network effects can carry them, but they will erode over time as the AI product experiences rapidly iterate and improve. One of the caveats is whether these incumbents can maintain a unique supply base with little multi-homing.
Olivia Moore provides a nice framework for thinking about the distribution of disruption, which suggests marketplaces with unique and hard-to-aggregate supply will benefit significantly from the shift to AI.
Marketplaces where AI can replace supply are at risk of immediate disruption. We have seen this with businesses like Chegg and Stack Overflow and labor marketplaces with low-complexity digital labor products.
It is essential to realize that while certain businesses can benefit (dark green), they may not be able to harness AI promptly and effectively to prevent disruption. Conversely, companies with AI headwinds (red) may lean into AI and disrupt themselves. Outside of physical supply, there is a world where AI agents replace the supply by becoming the supply.
The response and use of AI will dictate the outcomes, and those willing to adapt will thrive. Furthermore, AI allows startup competition to run faster and more efficiently, dramatically leveling the playing field.
I don’t think all marketplaces will be a perfect fit for the agent-led model, nor is it a foregone conclusion that they will happen. I see the following barriers to the creation and adoption of agent-led marketplaces.
Given these barriers for agent-led marketplaces, it is essential to ask: "Why now?"
The technological underpinnings for agent-led marketplaces are already here, and the green shoots are beginning to sprout. Large Language Models (LLMs) have shown they can handle complex reasoning and natural language interaction. Agents and recommendation systems are becoming more adept at understanding context, nuance, and intent. It is also increasingly clear that people like talking to agents and seek more human-like AI products, e.g., AI girlfriends and avatars.
The real challenge is not the technology but the design of systems that integrate these capabilities into a cohesive marketplace experience. Not all LLMs are great at everything, and an agent might need to use multiple LLMs for a given workflow. Platform operators must shift their mindsets. They must see AI not as a productivity tool but as a core enabler of their business model.
AI has a substantial market pull, making it the perfect time to build an agent-led marketplace for those willing to take risks. We can see the tiniest glimpses of the future, and there will likely be a significant upside for those willing to be a first-mover—a once-in-a-lifetime opportunity to build a generational marketplace.
The big question is when these marketplaces might start to appear. I am already starting to see them pop up, but they are few and far between. As a former podcast guest of mine, swyx puts it.
Expecting agent-led marketplaces to appear in 2025 magically would be a heroic assumption. This is not because they can’t, but because I think the technology is still being adopted and improved, and the human talent to pull these agents together is not ubiquitous enough. There is a very narrow cross-section of individuals who could pull this off. Given that, I predict (with an uncertain confidence interval) that we will start to see a few emerge this year, but 2026-2027 will be a ramping year for these marketplaces.
What do you do as a founder and operator to start this transition to an agent-led marketplace? There are many ways to go about it, but Ryan Moser shared a few ideas and real-world examples of how ThredUp implemented AI before LLMs to power its growth.
1. Understanding SKUs and Customers—ThredUp uses AI to cluster millions of SKUs based on customer preferences, allowing it to understand supply/demand imbalances.
2. AI-Driven Pricing—Listing prices are set intelligently using AI, which analyzes recent sales and pricing to optimize for revenue in a dynamic resale market.
3. Dynamic Price Adjustments with Reinforcement Learning—AI systems gradually lower their prices for items that don’t sell immediately. A reinforcement learning model provides continuous feedback, improving the pricing system over time and balancing revenue with inventory velocity.
4. Supply and Demand Optimization—ThredUp’s AI monitors supply and demand signals to optimize operations. For example, the system prioritizes high-performing supply sources while limiting consistently underperforming sources.
5. Personalized Search and Sorting—Customer embeddings are continuously refined using browsing and search history data. This personalization improves search and filter rankings, making finding relevant inventory in an extremely deep catalog easier.
6. Smarter Visual Search with CLIP Models—ThredUp employs CLIP (Contrastive Language–Image Pretraining) models to extract concepts from product photos. This enables richer search terms and improves discoverability, giving customers a more intuitive shopping experience.
Of course, many other areas have clear applications for agentic agents, but these are real-world applications of AI in marketplaces today. This section provides a taste of what is possible and warrants its own post on this topic.
Agent-led marketplaces represent a fundamental shift in how we think about commerce and connection. By leveraging AI’s unique strengths in aggregation and matching, these systems have the potential to move beyond efficiency gains to create entirely new forms of value.
The transition won’t happen overnight, raising important questions about trust, fairness, and control. But the potential is too great to ignore. As AI continues to evolve, tomorrow's marketplaces may no longer be places where buyers and sellers meet—they will be intelligent systems that anticipate our needs, act on our behalf, and make matching supply and demand as seamless as possible. They might even replace the supply they set out to aggregate.
AI is the future of marketplaces. And it’s closer than we think.
Please share your thoughts in the comments below!
You can connect with Colin to discuss this post in the Everything Marketplaces community here and also reference the post on his Substack here. A big thanks to Colin for also being active in the community, where he is often sharing his marketplace experience, insights, and helping early stage founders.
